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beamformit_step_by_step_sample11_almost_same.html
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'S1'><span style="white-space: pre;"><span>dirname = </span><span style="color: rgb(160, 32, 240);">'sample11/'</span><span>;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>outname = [dirname, </span><span style="color: rgb(160, 32, 240);">'enhan.wav'</span><span>];</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>orig_outname = [dirname, </span><span style="color: rgb(160, 32, 240);">'orig_enhan.wav'</span><span>];</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>paths = csvimport([dirname, </span><span style="color: rgb(160, 32, 240);">'wav.list'</span><span>],</span><span style="color: rgb(160, 32, 240);">'columns'</span><span>, {</span><span style="color: rgb(160, 32, 240);">'path'</span><span>});</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>[x1, </span><span class="warning_squiggle_rte warningHighlight">sr</span><span>] = audioread(paths{1});</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>[x2, </span><span class="warning_squiggle_rte warningHighlight">sr</span><span>] = audioread(paths{2});</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>[x3, </span><span class="warning_squiggle_rte warningHighlight">sr</span><span>] = audioread(paths{3});</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>[x4, </span><span class="warning_squiggle_rte warningHighlight">sr</span><span>] = audioread(paths{4});</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>[x5, sr] = audioread(paths{5});</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>x = [x1, x2, x3, x4, x5];</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>x = x';</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>nsample = size(x,2);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>nmic = 5;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(34, 139, 34);">%npair = nmic - 1;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>npair = nmic;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper outputs"><div class = 'S2'><span style="white-space: pre;"><span>figure;plot(x(1,:));</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsFigure" uid="92059795" data-scroll-top="null" data-scroll-left="null" data-testid="output_0" style="width: 1218px;"><div class="figureElement"><div class="figureContainingNode" style="width: 560px; max-width: 100%; display: inline-block;"><div class="GraphicsView" data-dojo-attach-point="graphicsViewNode,backgroundColorNode" id="uniqName_197_78" widgetid="uniqName_197_78" style="width: 100%; height: auto;"><img class="ImageView figureImage" data-dojo-attach-point="imageViewNode" draggable="false" ondragstart="return false;" id="uniqName_197_80" widgetid="uniqName_197_80" 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" style="width: 100%; height: auto;"></div></div></div></div></div></div></div><h2 class = 'S4'><span>make hamming window</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S0'><span style="white-space: pre;"><span>nwin = 16000; </span><span style="color: rgb(34, 139, 34);">% 1 sec</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(34, 139, 34);">% hamm_val = 0.54 - 0.46*cos(6.283185307*i/(window-1));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>win = zeros(1,nwin);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>i = 1:nwin</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> win(i) = 0.54 - 0.46 * cos(6.283185307*(i-1)/(nwin-1));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper outputs"><div class = 'S2'><span style="white-space: pre;"><span>figure; plot(win);</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsFigure" uid="774B5ABE" data-scroll-top="null" data-scroll-left="null" data-testid="output_1" style="width: 1218px;"><div class="figureElement"><div class="figureContainingNode" style="width: 560px; max-width: 100%; display: inline-block;"><div class="GraphicsView" data-dojo-attach-point="graphicsViewNode,backgroundColorNode" id="uniqName_197_81" widgetid="uniqName_197_81" style="width: 100%; height: auto;"><img class="ImageView figureImage" data-dojo-attach-point="imageViewNode" draggable="false" ondragstart="return false;" id="uniqName_197_83" widgetid="uniqName_197_83" 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evUqVMcPXqUW2+9la7jx4/Tbrfp+s53vsN73vMennvuObq+8pWvMD8/zx133IFIP4niDhP7n6I6NoysrOrYZiozLUTk2gmkQAiB3bt3s3PnTsbHx9m+fTsPPfQQ69ato2vPnj3Mzc3Rlc1m+d3f/V3e8Y538L73vY/f+q3f4uGHH+Ynf/InEekn9WbC0OD1FPMZZGUNDQ7QVZluISLXRiAltm7dytzcHLVajfn5ebZt28YLqtUqd955Jy+4++67OXbsGH/+53/O4cOH2bp1KyL9JIo71BptqmPDSG+ojg0zNd+m3kwQkeUXEJG+EsUdJvY/RXk0h/SW6tgwlekWIrL8AiLSV+rNhKHB6ynmM0hvKeYzdFWmW4jI8gqISN+I4g6VmRblkRzSmw7fdzMPzrSI4g4isnwCItI3JvY/RXkkx9DgANK7Dt/380zsfwoRWT4BEekLU402XaVCFultxXyGrnozQUSWR0BEel4Ud6g12pRHc0h/OHzfzdz+yFcQkeUREJGeV2u0GRq8nmI+g/SPB0dyTOw/iYgsvYCI9LQo7jA136Y6Noz0l/Jojig+R72ZICJLKyAiPW1i/1NUx4aR/lQezVGZbiEiSysgIj1rqtGmq5jPIP2pmM/QNdVoIyJLJyAiPSmKO9QabcqjOaS/Vcc2U5lpISJLJyAiPanWaDM0eD3FfAbpb0ODA5RHclSmW4jI0giISM+J4g71ZkJ1bBhJh1Ihy9R8m3ozQUSuXkBEek5lpsV4IYukS3VsmMp0CxG5egER6Sn1ZkIUn6NUyCLpUsxn6Ko3E0Tk6gREpKfUGmcoj+aQdCqP5ph95iwicnUCItIzphptuor5DJJOxXyGKOkw1WgjIlcuICI9IYo7VGZalEdySLpVx4apzLSI4g4icmUCItITKjMtivkMQ4MDSPqVbslSmWkhIlcmICIrLoo7RPE5qmPDyOpQHs1RbybUmwki8vIFRGTFTex/ivFCFlldqmPD1BpnEJGXLyAiK6reTBgavJ5SIYusLsV8hig+R72ZICIvT0BEVlRlukV5JIesTuXRHJXpFiLy8gREZMXUmwldQ4MDyOpUzGfoqjcTROTyBVLk9OnTHDp0CHfnpURRxKFDhzh58iQiKyGKO9QaZyiP5pDVrTyaY2L/SUTk8gVS4uDBg4yNjTE9Pc29997L3r17uZhqtcqv/MqvMD09zW/+5m/yoQ99CJFrrd5MiOJzFPMZZHUr5jMMZQaYarQRkcsTSIGFhQXK5TK1Wo1du3Zx4MABqtUqURRxocXFRXbv3s3U1BS7du3is5/9LJ/73Oc4efIkItdKFHeoNdqUR3OIdFXHNjOx/yQicnkCKfD444+zdu1aNm7cSNfg4CC33XYbR44c4cX867/+KwMDA3Rdf/31hBD4wQ9+wKWYGWaGmbFv3z5Erka9mVDMZyjmM4h0DQ0OUCpkqUy3ELka+/btw8wwM8yMtAqkwNmzZ9m0aRPnu+GGG3j66ae5UAiBcrnMfffdx969e3nve9/LXXfdxU033cSluDvujrszOTmJyJWK4g6VmRbjhSwi56uODfPgTIso7iBypSYnJ3F33B13J60CKbCwsEAIgfOFEFhcXOTFzM/P8xM/8RPceOONrF27lmazyfe+9z1EroVao03plixDgwOIXOjBkRy1RhsRubRACqxZs4aFhQXOt7i4yHXXXceFvvjFL/KVr3yFz3zmM7znPe/h0UcfpetTn/oUIsstijtMzbcpj+YQeTHl0RwPzrSI4g4icnGBFFi/fj0nTpzgfEmSsGXLFi6UJAmve93reMUrXsELXvva13L69GlEllut0aaYzyByKQ+O5KjMtBCRiwukQKFQoGt2dpauU6dOcfToUW699Va6jh8/Trvdpmvz5s3Mzc3xjW98g67nn3+e+fl53vCGNyCynKK4w9R8m+rYMCKXUh7NUW8miMjFBVIghMDu3bvZuXMn4+PjbN++nYceeoh169bRtWfPHubm5ugaHh5m586dvPvd72Z8fJy3vvWt/MIv/AK//Mu/jMhyqjXalG7JInI5yiM5JvafREReXCAltm7dytzcHLVajfn5ebZt28YLqtUqd955Jy+46667mJ+fp1arMT8/z/3334/IcoriDvVmQnk0h8jlKBWy1JsJUdxBRH5cQESWXWWmxXghi8jLUR7JUZlpISI/LiAiy67eTCgVsoi8HKVClnozod5MEJEfFRCRZVWZblEdG0bkSpRHcsw+cxYR+VEBEVlWU/NtivkMIleiVMgyNd+m3kwQkf8QEJFlc/sjxyiP5BC5GuWRHLXGGUTkPwREZFlEcYco6VAqZBG5GqVClnozod5MEJF/ExCRZTGx/ynKIzlElkJ1bJha4wwi8m8CIrLkorhDlHQoFbKILIViPkO9mVBvJogIBERkydUabcojOUSWUnkkx+wzZxERCIjIkoriDvVmQqmQRWQplQpZpubbRHEHkdUuICJLqjLTYryQRWQ5lEdyVGZaiKx2ARFZMlHcod5MKBWyiCyHUiFLvZkQxR1EVrOAiCyZykyL8kgOkeVUHslRmWkhspoFRGRJRHGHKD5HqZBFZDmVClnqzYQo7iCyWgVEZEnUGm2GBq9H5Fqojg1TmWkhsloFROSqRXGHqfk21bFhRK6FYj7DVKNNFHcQWY0CInLVKjMtSrdkEbmWHhzJUZlpIbIaBUTkqkRxh3ozYbyQReRaKo/mqDcToriDyGoTEJGrEiXnKOYzDA0OIHKtlW7JUplpIbLaBETkikVxh8p0i/JIDpGVUB7NMdVoI7LaBETkitWbCUOD1zM0OIDISikVskw12oisJgERuSJR3KHWaDNe2IDISiqP5JjYfxKR1SQgIlek3kzoKuYziKykocEBSoUs9WaCyGoRSJHTp09z6NAh3J2LieOY+fl55ufnmZ+fZ35+nvn5eaIoQuRyRXGH2eZZyqM5RHrBeGEDtz/yFURWi0BKHDx4kLGxMaanp7n33nvZu3cvL2Z+fp577rmHe+65h3vuuYd77rmHu+++m6mpKUQuV72ZEMXnKOYziPSCYj5DMb+WqUYbkdUgkAILCwuUy2VqtRq7du3iwIEDVKtVoijiQiMjIxw7doxjx45x7NgxPvaxj3HjjTfyG7/xG4hcrlqjTXVsMyK9pDyao9ZoI7IaBFLg8ccfZ+3atWzcuJGuwcFBbrvtNo4cOcKlfO973+P+++/nD/7gD1i7di2XYmaYGWbGvn37kNWr3kzoGhocQKSXFPMZhgavp95MkNVr3759mBlmhpmRVoEUOHv2LJs2beJ8N9xwA08//TSX8olPfIJNmzbx5je/mZfi7rg77s7k5CSyelWmW5RHc4j0orfk11KZbiGr1+TkJO6Ou+PupFUgBRYWFgghcL4QAouLi1zM97//farVKh/84AcRuVz1ZkKUdCjmM4j0olIhS1e9mSCSZoEUWLNmDQsLC5xvcXGR6667jov5u7/7O17zmtfw+te/HpHLVZluUR7JIdLLxgtZao0ziKRZIAXWr1/PiRMnOF+SJGzZsoWLmZ2d5Y477kDkckVxhyjpUCpkEellpUKWejOh3kwQSatAChQKBbpmZ2fpOnXqFEePHuXWW2+l6/jx47Tbbc73pS99iZtuugmRy1WZaVEeySHSD8ojOWqNM4ikVSAFQgjs3r2bnTt3Mj4+zvbt23nooYdYt24dXXv27GFubo4XLC4u8k//9E9s3rwZkcsRxR3qzYRSIYtIPygVskw12kRxB5E0CqTE1q1bmZubo1arMT8/z7Zt23hBtVrlzjvv5AUhBNydG2+8EZHLUWu0KeYziPSTB0dy1BptRNIoICKXFMUdpubblEdyiPST8miOB2daiKRRQEQuqd5MKOYzDA0OINJvSoUsU402ImkTEJGLiuIOtUab8cIGRPpReSTHxP6TiKRNQEQuqt5M6CrmM4j0o6HBAYr5tUw12oikSUBELqrWaFMezSHSz8qjOSozLUTSJCAiL6reTIiSDsV8BpF+VsxnGMoMEMUdRNIiICIvqjLdojo2jEgajBeyVGZaiKRFQER+TBR3iJIOxXwGkTQoFbLUmwn1ZoJIGgRE5MdUZlqUR3KIpEl5JEetcQaRNAiIyI+I4g71ZkKpkEUkTUqFLPVmQhR3EOl3ARH5EbVGm2I+g0galW7JUplpIdLvAiLy76K4w9R8m/JIDpE0Ko/mmGq0Eel3ARH5d1FyjtItWYYGBxBJq1Ihy1SjjUg/C4jIv6s1zvCWjWsRSbPySI6J/ScR6WcBEfmf6s2EKD5HMZ9BJM2GBgcoFbLUmwki/SogIv9TrXGG8mgOkdVgvLCBWuMMIv0qICLUmwlRfI5iPoPIalDMZ4jic0RxB5F+FBARKtMtxgtZRFaT8UKWykwLkX4UEFnlorhDlHQoFbKIrCalQpYoPkcUdxDpNwGRVa4y06KYzyCyGo0XslRmWoj0m4DIKhbFHerNhPJIDpHVqFTIUm8mRHEHkX4SEFnF6s2EYj7D0OAAIqtVMZ+h1mgj0k8CIqtUFHeoNdqMFzYgspqVR3I8ONNCpJ8EUuT06dMcOnQId+elxHHMF7/4Rb785S8jq1OUnKOrmM8gspoNDQ5QKmSZarQR6ReBlDh48CBjY2NMT09z7733snfvXi5mdnaWd77znXz+859n9+7dvPe972VxcRFZXWqNM5RHc4gIjBc2MNs8i0i/CKTAwsIC5XKZWq3Grl27OHDgANVqlSiKuNDCwgL3338/e/bsYffu3fzlX/4lZ8+eZWZmBlk96s2EKD5HMZ9BRKCYzxDF56g3E0T6QSAFHn/8cdauXcvGjRvpGhwc5LbbbuPIkSNcaHZ2lp/+6Z/mDW94Ay/427/9W7Zt24asHpXpFuOFLCLyH8YLWWafOYtIPwikwNmzZ9m0aRPnu+GGG3j66ae5UJIkvOY1r+HDH/4wN910EzfffDOf/OQneSlmhplhZuzbtw/pX1HcIUo6lApZROQ/lApZpubbRHEH6V/79u3DzDAzzIy0CqTAwsICIQTOF0JgcXGRCz3zzDNMT0/zcz/3cxw/fpzHHnuMP/7jP+bIkSNcirvj7rg7k5OTSP+qzLQo3ZJFRH5c6ZYstUYb6V+Tk5O4O+6Ou5NWgRRYs2YNCwsLnG9xcZHrrruOC/3Mz/wMr33ta7nrrrvoMjPuuOMOPv/5zyPpF8Ud6s2E8UIWEflx5dEcD860EOl1gRRYv349J06c4HxJkrBlyxYu9FM/9VNcKIRACAFJv3ozoZjPMDQ4gIi8uFIhy1SjjUgvC6RAoVCga3Z2lq5Tp05x9OhRbr31VrqOHz9Ou92m6/bbbyeOYw4fPkxXHMc88cQTvOtd70LSLYo71BptxgsbEJGLGy9sYGL/SUR6WSAFQgjs3r2bnTt3Mj4+zvbt23nooYdYt24dXXv27GFubo6uV77ylTz88MP83u/9HmNjY4yMjHDXXXexdetWJN2i5BxdxXwGEbm4Yj5DMb+WqUYbkV4VSImtW7cyNzdHrVZjfn6ebdu28YJqtcqdd97JC2655RYOHz7M/v37mZ+f57777kPSr9Y4Q3k0h4i8tPFCllqjjUivCoisAlHcod5MKOYziMhLKxWyREmHejNBpBcFRFaBykyL6tgwInL5yiM5ao0ziPSigEjKRXGHejOhmM8gIpevVMgy1Wgj0osCIilXa7Qp5jOIyMtXKmSZ2H8SkV4TEEmxKO5QbyaMFzYgIi9fdWyYKD6HSK8JiKRYvZnQVcxnEJErMzR4PVONNiK9JCCSYrVGm/JoDhG5cuOFDdQabUR6SUAkperNhCjpUMxnEJErV8xn6Ko3E0R6RUAkpWqNM5RHcojI1RsvZKk1ziDSKwIiKRTFHerNhFIhi4hcvVIhS72ZEMUdRHpBQCSFKjMtSrdkEZGlUx7JUZlpIdILAiIpE8Ud6s2E8mgOEVk6pUKWqUabKO4gstICIilTbyaUbskiIkuvVMhSbyaIrLSASIpEcYfZ5kmYi/kAACAASURBVFnGC1lEZOmVR3JM7D+JyEoLiKRIlJwjis8xNDiAiCy9ocEBivm1TDXaiKykgEiKVKZblEdziMjyGS9kmW2eRWQlBURSIoo7REmHYj6DiCyfUiFLvZlQbyaIrJSASEpUZlqUR3KIyPIrj+SoNc4gslICIikQxR3qzYRSIYuILL9SIUu9mSCyUgIiKVBrtCnmM4jItVPMZ5jYfxKRlRAQ6XNR3GFqvs14YQMicu1Ux4aZarQRWQkBkT5XbyYMZQYo5jOIyLVVKmSZarQRudYCIn2u1mhTHs0hItfeeGEDlZkWItdaQKSP1ZsJUdKhmM8gItdeMZ9hKDNAvZkgci0FUuT06dMcOnQId+dS4jhmfn6e+fl55ufnmZ+f5/nnn0f6T2W6RXVsGBFZOeOFLLPPnEXkWgqkxMGDBxkbG2N6epp7772XvXv3cjGf+9znGB8f55577uGee+7hnnvu4R/+4R+Q/hLFHbqK+QwisnJKhSxT822iuIPItRJIgYWFBcrlMrVajV27dnHgwAGq1SpRFPFivv71r/PAAw9w7Ngxjh07xrFjx3jTm96E9JfKTItiPoOIrLzySI7KTAuRayWQAo8//jhr165l48aNdA0ODnLbbbdx5MgRXsxTTz1FPp8njmN++MMfcjnMDDPDzNi3bx+ysqK4Q72ZUB7NISIrr1TIMtVoE8UdZGXt27cPM8PMMDPSKpACZ8+eZdOmTZzvhhtu4Omnn+ZCCwsL/OM//iO///u/zzvf+U5uuukmPvShD/FS3B13x92ZnJxEVla9mVDMZxCR3lEqZKk12sjKmpycxN1xd9ydtAqkwMLCAiEEzhdCYHFxkQs999xzvP3tb+dP/uRPOHr0KIcPH+aJJ57gscceQ/pDFHeYbZ5lvLABEekd5ZEcD860ELkWAimwZs0aFhYWON/i4iLXXXcdF3r1q1/Nxz72MV796lfT9apXvYo77riDv//7v0f6Q5ScI4rPUcxnEJHeMTQ4QDG/lqlGG5HlFkiB9evXc+LECc6XJAlbtmzhQs8++ywHDhzgfD/4wQ94xStegfSHynSL8mgOEek95dEctUYbkeUWSIFCoUDX7OwsXadOneLo0aPceuutdB0/fpx2u01Xp9OhXC7zzDPP0PXcc8/xhS98gXe9611I76s3E7qK+Qwi0nuK+Qxd9WaCyHIKpEAIgd27d7Nz507Gx8fZvn07Dz30EOvWraNrz549zM3N0WVmPPDAA7z73e9mfHycd7zjHbz//e/nTW96E9L7KtMtxgtZRKR3jRey1BpnEFlOgZTYunUrc3Nz1Go15ufn2bZtGy+oVqvceeedvOA973kPx44do1arcezYMSYmJpDeF8UdoqRDqZBFRHpXqZClK4o7iCyXgEifqDXaFPMZRKT3DWUGqMy0EFkuAZE+EMUdpubblEdyiEjvK4/mmGq0EVkuAZE+UG8mFPMZhgYHEJH+UCpkmWq0EVkOAZEeF8Udao0244UNiEj/KI/kmNh/EpHlEBDpcVFyjqHB6ynmM4hI/xgaHKBUyDLVaCOy1AIiPa7WOMN4YQMi0n/GCxuoNdqILLWASA+L4g5RfI5iPoOI9J9iPkNXvZkgspQCIj2sMtOiPJpDRPpXeTRHrXEGkaUUEOlRUdyh3kwo5jOISP8q5jNE8TmiuIPIUgmI9KjKTItiPoOI9L9iPkOt0UZkqQREelAUd6g3E8ojOUSk/5VHc0zNtxFZKgGRHhQl5yjmMwwNDiAi6VDMZ5hqtBFZCgGRHlRrnGG8sAERSY/xwgYm9p9EZCkERHpMvZkQxeco5jOISHoU8xmK+bVMNdqIXK2ASI+pNc5QHs0hIulTHs0x2zyLyNUKiPSQejOh3kwo5jOISPoU8xmi+Bz1ZoLI1QiI9JDKdIvySA4RSa/xQpbZZ84icjUCIj0iijtESYdSIYuIpFepkGVqvk0UdxC5UgGRHlGZaVG6JYuIpF95JEdlpoXIlQqI9IAo7lBvJpRHc4hI+pUKWaYabUSuVECkB9SbCcV8BhFZPUqFLJXpFiJXIiCywqK4Q63RZrywARFZPapjwzw400LkSgREVli9mdBVzGcQkdWlVMgy1Wgj8nIFUuT06dMcOnQId+dyHT9+nG9961vIyojiDrVGm/JoDhFZfcYLG6jMtBB5uQIpcfDgQcbGxpienubee+9l7969vJRnnnmG9773vRw/fhxZGVFyjq5iPoOIrD7FfIahzABTjTYiL0cgBRYWFiiXy9RqNXbt2sWBAweoVqtEUcTF/PCHP+S3f/u3WbduHbJyKtMtyqM5RGT1Ko/mmG2eReTlCKTA448/ztq1a9m4cSNdg4OD3HbbbRw5coSL+ehHP8rb3vY2Xve613E5zAwzw8zYt28fcvXqzYQo6VDMZxCR1auYzxDF56g3E+Tq7du3DzPDzDAz0iqQAmfPnmXTpk2c74YbbuDpp5/mxTz55JN8+ctf5oMf/CCXy91xd9ydyclJ5OpVpluUR3KIiIwXstQaZ5CrNzk5ibvj7rg7aRVIgYWFBUIInC+EwOLiIhd6/vnn+fCHP8xHP/pRZOVEcYco6VAqZBERKRWy1JsJ9WaCyOUIpMCaNWtYWFjgfIuLi1x33XVc6L//9//O5s2befbZZ5mdnSWOY77+9a/j7si1U5lpUR7JISLygvJIjlrjDCKXI5AC69ev58SJE5wvSRK2bNnChW688Ua++93v8pnPfIbPfOYzfPOb32R2dpajR48i10YUd6g3E0qFLCIiLygVskw12kRxB5GXEkiBQqFA1+zsLF2nTp3i6NGj3HrrrXQdP36cdrtN16//+q/z6KOP8uijj/Loo4/y+te/nvvuu4+JiQnk2qjMtCjmM4iIXOjBkRyVmRYiLyWQAiEEdu/ezc6dOxkfH2f79u089NBDrFu3jq49e/YwNzeHrLwo7lBvJpRHcoiIXKg8mmOq0SaKO4hcSiAltm7dytzcHLVajfn5ebZt28YLqtUqd955Jy/m0Ucf5e1vfztybdSbCcV8hqHBAUREXkypkKXWaCNyKQGRaySKO9QabcYLGxARuZjq2DAPzrQQuZSAyDVSbyZ0FfMZREQupVTIMtVoI3IxAZFrIIo71BptyqM5REReSnkkx8T+k4hcTEDkGqg3E4YGr6eYzyAi8lKGBgco5tcy1Wgj8mICItdArdFmvLABEZHLVR7NUWu0EXkxAZFlVm8mDA1eTzGfQUTkchXzGYYGr6feTBC5UEBkmVWmW4wXNiAi8nK9Jb+WynQLkQsFRJZRvZnQVcxnEBF5uUqFLF31ZoLI+QIiy6gy3aI8mkNE5EqNF7LUGmcQOV9AZJlEcYeuYj6DiMiVKhWyRPE56s0EkRcERJZJZabFeCGLiMjVKo/mqDXOIPKCgMgyiOIOXaVCFhGRq1XMZ+iK4g4iXQGRZTCx/ynekl+LiMhSeUt+LZWZFiJdAZElFsUdoqRDqZBFRGSplApZ6s2EKO4gEhBZYpWZFuWRHCIiS608kqMy00IkILKEorhDvZlQKmQREVlqpUKWejMhijvI6hYQWUKVmRblkRwiIsulOjZMZaaFrG4BkSUSxR3qzYRSIYuIyHIp5jPUmwlR3EFWr4DIEqnMtCjmM4iILLfySI7KTAtZvQIiSyCKO9SbCdWxYUREllupkKXeTJDVKyCyBCozLYr5DCIi10p5JMfE/pPI6hQQuUpR3KHeTKiODSMicq2UClmi+BxR3EFWn4DIVarMtCjdkkVE5FobL2SpzLSQ1SeQIqdPn+bQoUO4Oy/F3Tl06BBRFCFXLoo71JsJ5dEcIiLXWqmQpd5MiOIOsroEUuLgwYOMjY0xPT3Nvffey969e7mYP/qjP2JycpIvfOELvP/97+fRRx9FrkxlpkXpliwiIiulPJKjMtNCVpdACiwsLFAul6nVauzatYsDBw5QrVaJoogLnTp1ik996lN89rOf5SMf+QiPPfYYe/fuJY5j5OWJ4g71ZkJ5NIeIyEopFbLUmwlR3EFWj0AKPP7446xdu5aNGzfSNTg4yG233caRI0e4UD6f53Of+xxr166l65WvfCULCwv88Ic/RF6eykyL8kgOEZGVVh7JUZlpIatHIAXOnj3Lpk2bON8NN9zA008/zYVCCGzcuJGFhQX+4i/+gvHxcX7t136NV73qVVyKmWFmmBn79u1jtYviDlF8jlIhi4jISisVskTxOerNhNVu3759mBlmhpmRVoEUWFhYIITA+UIILC4ucjFxHPP973+f9evXMzc3x9mzZ7kUd8fdcXcmJydZ7SozLcqjOUREesV4IUutcYbVbnJyEnfH3XF30iqQAmvWrGFhYYHzLS4uct1113ExN954I3fffTef+MQnGBgYoFarIZcnijvUmwnFfAYRkV5RKmSJ4nPUmwmSfoEUWL9+PSdOnOB8SZKwZcsWLvSNb3yDT3/605xvw4YNnDlzBrk8E/ufojySQ0Sk14wXstQaZ5D0C6RAoVCga3Z2lq5Tp05x9OhRbr31VrqOHz9Ou92ma2FhgY985CN84xvfoOvb3/42R44c4Y477kBeWhR3iJIOpUIWEZFeUypkieJz1JsJkm6BFAghsHv3bnbu3Mn4+Djbt2/noYceYt26dXTt2bOHubk5un72Z3+WD33oQ/zSL/0Sv/qrv8rb3/527r77bt761rciL21i/1OUR3KIiPSq8UKWynQLSbdASmzdupW5uTlqtRrz8/Ns27aNF1SrVe68805esH37dr761a/yyU9+kq9+9at84AMfQF5avZkQJR1KhSwiIr2qVMjSVW8mSHoFRC5TZbpFdWwYEZFeVx7NUZluIekVELkM9WZCVzGfQUSk1xXzGbqmGm0knQIiLyGKO1SmW5RHc4iI9IvyaI7KTAtJp4DIS6g3E7qK+QwiIv2imM8wlBlgqtFG0icgcglR3KHWaFMezSEi0m+qY5uZ2H8SSZ+AyCVEyTmGBq+nmM8gItJvhgYHKBWyVKZbSLoERC4iijtM7D9JeSSHiEi/qo4NMzXfJoo7SHoERC6iMtOidEuWocEBRET6WXkkR2WmhaRHQORFRHGHejOhPJpDRKTflQpZ6s2EejNB0iEg8iIqMy3KIzlERNKiPJKjMt1C0iEgcoF6M6HeTCgVsoiIpEWpkKWr3kyQ/hcQuUBlukV1bBgRkbQpj+aY2H8S6X8B+X/bg8PQuO8z39uf84uKbKEXk6kqjiltbCx6y65LOVYnQllQ4raJ9aJ9UU7ZjLKhsvuidEzTspRQPLt4qtIGlJkTLGysGtKdDAXX3YouZUpgwhB7ZGkgcvBiqsZ8JXs9YWj9lIJmaMmuu+Y/+5w/55hnntTZ7XZjW5Lv63JdXrl4g53J7Tyx+2Gcc26reWL3w+x8eBuvXLyB29wCzv1fjfWblC7eIPfULpxzbqsqpvdy+OwV3OYWcO7/Kl28wc7kdnYmt+Gcc1vVzuQ2vv3ULg6fvYLbvALO/W+N9Zucv9aimN6Dc85tdbmDuzh/rcX5ay3c5hRw7n87fPYtplI7cM65B0XuqV1MV67jNqeAe+C9cvEGO5PbOZTagXPOPSgOpXYQO3+thdt8Au6B1li/Se1am6nUf8c55x40xfReDp+9gtt8Au6Bdv5ai9gTux/GOeceNDuT2zj0qR1MV67jNpeAe2A11m8y/dp1iuk9OOfcgyp3cBevvHmD89dauM0j4B5Yh8++Re6pXTjn3IOumN7DdOU6bvMIuAfSKxdvEDuU2oFzzj3ontj9MDuT23nl4g3c5hDYQprNJtVqFUn8R65evUq1WuXSpUs8aBrrNyldvEExvRfnnHP/R+6pXRw+ewW3OQS2iHK5TDqdplKpkMlkmJ2d5b1897vf5Stf+QqVSoXp6WmeeeYZ/vCHP/CgmH7tOk/sfpidyW0455z7P3Ymt/Htp3Zx+OwV3MYX2AKiKCKXy1Eqlcjn88zPz1MsFmk0GrzblStX+PGPf8xPf/pT8vk8P/vZz/j9739PuVzmQXD+Wovz11rkDu7COefc/1/u4C4a6//C+Wst3MYW2AIWFhZIJBIMDQ0RSyaTjI+Ps7i4yLslEglOnz5NIpHgtl27dvHrX/+ara6xfpPSxf+HYnoPzjnn7ix3cBfTleu4jS2wBbTbbYaHh+nW39/P6uoq77Zjxw4ee+wxbnv77bc5d+4cTz75JP8eM8PMMDNOnDjBZlS6eIPYE7sfxjnn3J09sfthntj9MIfPXmEzOnHiBGaGmWFmbFWBLSCKIkIIdAsh0Ol0+Pf85je/4dChQxw5coQ9e/bw75GEJCTx3HPPsdk01m/yyps3KKb34Jxz7t+XO7iLxvq/cP5ai83mueeeQxKSkMRWFdgCent7iaKIbp1Oh56eHt7LL37xC77whS/wpS99iUwmw1bWWL/J4bNvcS6zH+ecc3+aYnovh89ewW1MgS1gcHCQlZUVurVaLUZGRriTer3Ol7/8Zb797W9z+PBhtrrSxRvsTG5nZ3Ibzjnn/jQ7k9s49KkdTFeu4zaewBaQSqWI1Wo1Ymtra9TrdcbGxohdvnyZGzduEGs2m3zta1/jxRdf5MCBA9y6dYtbt24RRRFbUWP9Jq+8eYNieg/OOef+c3IHd3H+Wovz11q4jSWwBYQQKBQKZLNZpqammJycZGZmhoGBAWLHjx9naWmJ2JkzZ3jnnXf46le/yr59+9i3bx/79u3je9/7HltNY/0mpYs3KKb34Jxz7s+TO7iL6cp13MYS2CJGR0dZWlqiVCrx5ptvMjExwW3FYpEvfvGLxL71rW8hCUlIQhKSOHbsGFtN6eINYk/sfhjnnHN/nid2P8xUageHz17BbRwBtyWdv9bilTdvkDu4C+ecc/81h1I7iJ2/1sJtDAG35TTWbzJduU4xvQfnnHPvj9xTuzhw6h9xG0PAbTmHz77FVGoHT+x+GOecc++PncltnDvyP5iuXMfdfwG3pZy/1mJncjuHUjtwzjn3/npi98Ocv9ZiunIdd38F3JbRWL/J4bNXKKb34Jxz7u44d2Q/jdZNzl9r4e6fgNsSGus3mX7tOsX0Hpxzzt1dU6n/znTlOu7+CbgtYfq16zy+O8ETux/GOefc3fXE7ofJHdzFgVOXcPdHwG1605XrxA6lduCcc+7eeGL3w8ReuXgDd+8F3KZ2/lqLV968QTG9B+ecc/fWuSP7KV28wflrLdy9FXCbVmP9JofPXuFcZj/OOefuj2J6L4fPXuH8tRbu3gm4TamxfpPSxRucy+xnZ3Ibzjnn7o+dyW2cy+zn8NkruHsn4DadxvpNpl+7ziPJbexMbsM559z9tTO5jWJ6DwdOXcLdGwG36ZQu3mDnw9s4lNqBc865jeGJ3Q/zxO6HOXz2Cu7uC7hN5fy1Fo3WTXIHd+Gcc25jyR3cRWP9X5iuXMfdXQG3aZy/1qJ2tU0xvQfnnHMb07kj+zl/rcX5ay3c3RNwm0Jj/SbTlevkDu7COefcxnbuyH6mK9c5f62FuzsCbsNrrN/kwNwlium9OOec2xyK6b3UrrY5f62Fe/8F3IbWWL/JgblLnMvsZ2dyG8455zaHnclt5A7uYrpynfPXWrj3V8BtWI31mxw++xbnMvvZmdyGc865zaeY3suBU//I+Wst3Psn4DakxvpNDp99i2J6LzuT23DOObc57Uxu4/rfPMbhs1dorN/EvT8CbsNprN/k8Nm3KKb3sjO5Deecc5vbzuQ2rv/NYxw++xbnr7Vw/3UBt6G8cvEGB+YuUUzvZWdyG84557aOYnov05XrNNZv4v5rAm7DeOXiDaZfu865zH52JrfhnHNua9mZ3EYxvZfDZ99iunId9+cLuPuusX6T6cp1atfaXP+bx9iZ3IZzzrmtaWdyG+eO7Of8tRaHz17B/XkCW0iz2aRarSKJP9WFCxe4nxrrNzl/rcUjyW0U03twzjn3YDh3ZD+P705w4NQlGus3cf85gS2iXC6TTqepVCpkMhlmZ2f5j5w6dYpsNsv9cv5ai+nXrhM7lNqBc865B8uh1A6K6b0cmLvE+Wst3J8usAVEUUQul6NUKpHP55mfn6dYLNJoNLiTdrvN0aNHefnll7kfGus3eeXiDQ6fvULuqV0cSu3AOefcg2lnchvnMvuZrlxnunId96cJbAELCwskEgmGhoaIJZNJxsfHWVxc5E6OHz9OMpnkhRde4E9lZpgZZsaJEyf4czXWb3L47Fu8vX6T63/zGDuT23DOOfdg25ncxrkj+4kdOHWJ89da/LlOnDiBmWFmmBlbVWALaLfbDA8P062/v5/V1VXu5NixYzz//PNs376dP5UkJCGJ5557jv+sxvpNGus3mX7tOsX0XnIHd+Gcc851yx3cRe7gLmpX20xXrvPneO6555CEJCSxVQW2gCiKCCHQLYRAp9PhTkII3CuN9Zu8cvEGB+YuUbp4g2J6DzuT23DOOefu5IndD5M7uIvYf/vm65y/1sL9scAW0NvbSxRFdOt0OvT09HC/NNZvMl25zvRr16lda3Mus5/cwV0455xzf4rcwV382//6NLWrbQ6cusR05Tru/xPYAgYHB1lZWaFbq9ViZGSE++GVizc4fPYtYrmndlFM72FnchvOOefcf1bu4C7OHdnPI8ltHDh1ienKdRrrN3nQBbaAVCpFrFarEVtbW6NerzM2Nkbs8uXL3Lhxg7ulsX6TVy7eYLpynenKdWLF9F5yB3exM7kN55xz7r/qUGoHxfReYofPvsWBU5eYrlznQRXYAkIIFAoFstksU1NTTE5OMjMzw8DAALHjx4+ztLTE+6GxfpPz11q8cvEG05XrTFeuM/3adWrX2jw+lCB3cBeHUjvYmdyGc845937amdxG7uAuzh3ZTzG9l0eS2/hv33ydA6cucfjsFaYr1zl/rUVj/SZbXWCLGB0dZWlpiVKpxJtvvsnExAS3FYtFvvjFL/Jujz/+OBcuXOA/cv3T3+XAqUscOHWJw2ffYrpynbfXb/L4UILHhxIU03sopvfwxO6Hcc455+6FncltHErt4N/+16cppvfy+O4EsdrVNtOvXefAqUscPnuF65/+LltRwP2Hdr3+t5w7sp9zR/Zz7sh+zh3ZT+7gLp7Y/TBP7H4Y55xz7n7amdzGodQOcgd3kTu4i2J6D+eO7KeY3sOu1/+WrSjgnHPOObfJBJxzzjnnNpmAc84559wmE3DOOeec22QCzjnnnHObTMA555xzbpMJOOecc85tMgHnnHPOuU0m4Jxzzjm3yQScc8455zaZgHPOOefcJhNwzjnnnNtkAs4555xzm0zAOeecc26TCTjnnHPObTIB55xzzrlNJuCcc845t8kEnHPOOec2mYBzzjnn3CYTcM4555zbZALOOeecc5tMwD2wTpw4wVZ04sQJtqITJ06wVZ04cYKt6MSJE2xFJ06cYCs6ceIEbvMIPKCazSbVahVJPKhOnjzJVnTy5Em2opMnT7JVnTx5kq3o5MmTbEUnT55kKzp58iRu8wg8gMrlMul0mkqlQiaTYXZ2Fuecc85tHoEHTBRF5HI5SqUS+Xye+fl5isUijUYD55xzzm0OgQfMwsICiUSCoaEhYslkkvHxcRYXF3kvjz76KGaGmWFmmBlmhplhZpgZZoaZYWaYGWaGmWFmmBlmhplhZpgZZoaZYWaYGWaGmWFmmBlmhplhZpgZZoaZYWaYGWaGmWFmmBlmhplhZpgZZoaZYWaYGWaGmWFmmBlmRszMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzIiZGWaGmWFmmBlmhplhZpgZZoaZYWaYGWaGmWFmmBlmhplhZpgZZoaZYWaYGWaGmWFmmBlmhplhZpgZZoaZYWaYGWaGmWFmmBlmhplhZpgZZoaZYWaYGWaGmWFmmBkxM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzIiZGWaGmWFmmBlmhplhZpgZZoaZYWaYGWaGmWFmmBlmhplhZpgZZoaZYWaYGWaGmWFmmBlmhplhZpgZZoaZYWaYGWaGmWFmmBlmhplhZpgZZoaZYWaYGWaGmWFmmBkxM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDMjZmaYGWaGmWFmmBlmhplhZpgZZoaZYWaYGWaGmWFmmBlmhplhZpgZZoaZYWaYGWaGmWFmmBlmhplhZpgZZoaZYWaYGWaGmWFmmBlmhplhZpgZZoaZYWaYGWaGmWFmxMwMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMDDPDzDAzzAwzw8wwM8wMM8PMMDPMjEcffZStKPCAabfbDA8P062/v5/V1VXeyw9/+EMkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCwMQPGAAAHLFJREFUEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCQkIQlJSEISkpCEJCQhCUlIQhKSkIQkJCEJSUhCEpKQhCR++MMfshUFHjBRFBFCoFsIgU6ng3POOec2h8ADpre3lyiK6NbpdOjp6cE555xzm0PgATM4OMjKygrdWq0WIyMjOOecc25zCDxgUqkUsVqtRmxtbY16vc7Y2BjOOeec2xwCD5gQAoVCgWw2y9TUFJOTk8zMzDAwMIBzzjnnNofAA2h0dJSlpSVKpRJvvvkmExMTOOecc27zCDjnnHPObTIB55xzzrlNJuDeU7PZpFqtIomN7urVq1SrVS5dusS7NZtNqtUqkriTZrNJtVpFEu/WbDapVqtI4n67fPkyv/3tb+nWbDapVqtI4k6azSbVahVJvFuz2aRarSKJ+2F9fZ3XX3+dN954g3drNptUq1UkcSfNZpNqtYok3q3ZbFKtVpHE/dBoNKhWq1y5coV3azabVKtVJHEnzWaTarWKJN6t2WxSrVaRxL104cIF3q3ZbFKtVpHEnTSbTarVKpJ4t2azSbVaRRJ30mw2qVarSOJuu3DhAu929epVqtUqly5d4k6azSbVahVJvFuz2aRarSKJO2k2m1SrVSRxN124cIH3cvnyZX7729/ybs1mk2q1iiTerdlsUq1WkcSdNJtNqtUqkribLly4wLutr6/z+uuv88Ybb3AnzWaTarWKJN6t2WxSrVaRxJ00m02q1SqS2IgC7o7K5TLpdJpKpUImk2F2dpaN6rvf/S5f+cpXqFQqTE9P88wzz/CHP/yBWLlcJp1OU6lUyGQyzM7O0q1cLpNOp6lUKmQyGWZnZ7mtXC6TTqepVCpkMhlmZ2e5X65evcqzzz7L5cuXua1cLpNOp6lUKmQyGWZnZ+lWLpdJp9NUKhUymQyzs7PcVi6XSafTVCoVMpkMs7Oz3Eu1Wo3Pfe5zvPrqqxQKBZ599lk6nQ6xcrlMOp2mUqmQyWSYnZ2lW7lcJp1OU6lUyGQyzM7Oclu5XCadTlOpVMhkMszOznIvFYtF/uqv/opKpcJf//Vf87d/+7fcVi6XSafTVCoVMpkMs7OzdCuXy6TTaSqVCplMhtnZWW4rl8uk02kqlQqZTIbZ2VnuhVOnTpHNZulWLpdJp9NUKhUymQyzs7N0K5fLpNNpKpUKmUyG2dlZbiuXy6TTaSqVCplMhtnZWbqVy2XS6TSVSoVMJsPs7Cx3y6lTp8hms3T77ne/y1e+8hUqlQrT09M888wz/OEPf+C2crlMOp2mUqmQyWSYnZ3ltnK5TDqdplKpkMlkmJ2dpVu5XCadTlOpVMhkMszOznI3nDp1imw2y51cvXqVZ599lsuXL9OtXC6TTqepVCpkMhlmZ2e5rVwuk06nqVQqZDIZZmdn6VYul0mn01QqFTKZDLOzs9wNp06dIpvN0q1Wq/G5z32OV199lUKhwLPPPkun0+G2crlMOp2mUqmQyWSYnZ3ltnK5TDqdplKpkMlkmJ2dpVu5XCadTlOpVMhkMszOzrLRBNwfiaKIXC5HqVQin88zPz9PsVik0Wiw0Vy5coUf//jH/PSnPyWfz/Ozn/2M3//+95TLZaIoIpfLUSqVyOfzzM/PUywWaTQaxKIoIpfLUSqVyOfzzM/PUywWaTQaRFFELpejVCqRz+eZn5+nWCzSaDS4127dusU3v/lNBgYGuC2KInK5HKVSiXw+z/z8PMVikUajQSyKInK5HKVSiXw+z/z8PMVikUajQRRF5HI5SqUS+Xye+fl5isUijUaDeyGKIo4ePcrx48cpFAr85Cc/od1u89prrxFFEblcjlKpRD6fZ35+nmKxSKPRIBZFEblcjlKpRD6fZ35+nmKxSKPRIIoicrkcpVKJfD7P/Pw8xWKRRqPBvdDpdCgUCrzyyivk83n+/u//nn/4h3/gypUrRFFELpejVCqRz+eZn5+nWCzSaDSIRVFELpejVCqRz+eZn5+nWCzSaDSIoohcLkepVCKfzzM/P0+xWKTRaHC3tNttjh49yssvv0y3KIrI5XKUSiXy+Tzz8/MUi0UajQaxKIrI5XKUSiXy+Tzz8/MUi0UajQZRFJHL5SiVSuTzeebn5ykWizQaDWJRFJHL5SiVSuTzeebn5ykWizQaDd5P7Xabo0eP8vLLL9PtypUr/PjHP+anP/0p+Xyen/3sZ/z+97+nXC4Ti6KIXC5HqVQin88zPz9PsVik0WgQRRG5XI5SqUQ+n2d+fp5isUij0SAWRRG5XI5SqUQ+n2d+fp5isUij0eD90m63OXr0KC+//DJ3cuvWLb75zW8yMDBAtyiKyOVylEol8vk88/PzFItFGo0GURSRy+UolUrk83nm5+cpFos0Gg1iURSRy+UolUrk83nm5+cpFos0Gg3eL+12m6NHj/Lyyy/TLYoijh49yvHjxykUCvzkJz+h3W7z2muvEYuiiFwuR6lUIp/PMz8/T7FYpNFoEEURuVyOUqlEPp9nfn6eYrFIo9EgFkURuVyOUqlEPp9nfn6eYrFIo9FgIwm4P7KwsEAikWBoaIhYMplkfHycxcVFNppEIsHp06dJJBLctmvXLn7961+zsLBAIpFgaGiIWDKZZHx8nMXFRWILCwskEgmGhoaIJZNJxsfHWVxcZGFhgUQiwdDQELFkMsn4+DiLi4vcay+99BKf+cxn+NjHPsZtCwsLJBIJhoaGiCWTScbHx1lcXCS2sLBAIpFgaGiIWDKZZHx8nMXFRRYWFkgkEgwNDRFLJpOMj4+zuLjIvVCr1fjwhz/Mo48+ym0///nPmZiYYGFhgUQiwdDQELFkMsn4+DiLi4vEFhYWSCQSDA0NEUsmk4yPj7O4uMjCwgKJRIKhoSFiyWSS8fFxFhcXuVf+7d/+jW3bthHbvn07IQT+9V//lYWFBRKJBENDQ8SSySTj4+MsLi4SW1hYIJFIMDQ0RCyZTDI+Ps7i4iILCwskEgmGhoaIJZNJxsfHWVxc5G45fvw4yWSSF154gW4LCwskEgmGhoaIJZNJxsfHWVxcJLawsEAikWBoaIhYMplkfHycxcVFFhYWSCQSDA0NEUsmk4yPj7O4uEhsYWGBRCLB0NAQsWQyyfj4OIuLi7yfjh8/TjKZ5IUXXqBbIpHg9OnTJBIJbtu1axe//vWviS0sLJBIJBgaGiKWTCYZHx9ncXGRhYUFEokEQ0NDxJLJJOPj4ywuLhJbWFggkUgwNDRELJlMMj4+zuLiIu+X48ePk0wmeeGFF7iTl156ic985jN87GMfo9vCwgKJRIKhoSFiyWSS8fFxFhcXWVhYIJFIMDQ0RCyZTDI+Ps7i4iKxhYUFEokEQ0NDxJLJJOPj4ywuLvJ+OX78OMlkkhdeeIFutVqND3/4wzz66KPc9vOf/5yJiQliCwsLJBIJhoaGiCWTScbHx1lcXGRhYYFEIsHQ0BCxZDLJ+Pg4i4uLxBYWFkgkEgwNDRFLJpOMj4+zuLjIRhJwf6TdbjM8PEy3/v5+VldX2Wh27NjBY489xm1vv/02586d48knn6TdbjM8PEy3/v5+VldXibXbbYaHh+nW39/P6uoq7Xab4eFhuvX397O6usq9tLy8zBtvvMHXv/51urXbbYaHh+nW39/P6uoqsXa7zfDwMN36+/tZXV2l3W4zPDxMt/7+flZXV7kXWq0WH/nIRzh27Bif/OQn2b9/Pz/4wQ+ItdtthoeH6dbf38/q6iqxdrvN8PAw3fr7+1ldXaXdbjM8PEy3/v5+VldXuRdCCORyOY4cOcLs7CzPPvssTz/9NJ/85Cdpt9sMDw/Trb+/n9XVVWLtdpvh4WG69ff3s7q6SrvdZnh4mG79/f2srq5ytxw7doznn3+e7du3063dbjM8PEy3/v5+VldXibXbbYaHh+nW39/P6uoq7Xab4eFhuvX397O6ukqs3W4zPDxMt/7+flZXV3k/HTt2jOeff57t27fTbceOHTz22GPc9vbbb3Pu3DmefPJJYu12m+HhYbr19/ezurpKu91meHiYbv39/ayurhJrt9sMDw/Trb+/n9XVVd4vx44d4/nnn2f79u282/LyMm+88QZf//rXebd2u83w8DDd+vv7WV1dpd1uMzw8TLf+/n5WV1eJtdtthoeH6dbf38/q6irvl2PHjvH888+zfft2urVaLT7ykY9w7NgxPvnJT7J//35+8IMfcFu73WZ4eJhu/f39rK6u0m63GR4eplt/fz+rq6vE2u02w8PDdOvv72d1dZWNJOD+SBRFhBDoFkKg0+mwkf3mN7/h0KFDHDlyhD179hBFESEEuoUQ6HQ6xKIoIoRAtxACnU6HKIoIIdAthECn0+Fe+d3vfsexY8d46aWXeLcoiggh0C2EQKfTIRZFESEEuoUQ6HQ6RFFECIFuIQQ6nQ73wtWrV6lUKnz84x/n8uXL/OhHP+L73/8+i4uLRFFECIFuIQQ6nQ6xKIoIIdAthECn0yGKIkIIdAsh0Ol0uFfefPNN+vr6+NCHPkQikeDatWv88z//M1EUEUKgWwiBTqdDLIoiQgh0CyHQ6XSIoogQAt1CCHQ6He6WEAJ3EkURIQS6hRDodDrEoigihEC3EAKdTocoiggh0C2EQKfTIRZFESEEuoUQ6HQ6vJ9CCPxHfvOb33Do0CGOHDnCnj17iEVRRAiBbiEEOp0OURQRQqBbCIFOp0MsiiJCCHQLIdDpdHi/hBC4k9/97nccO3aMl156iTuJoogQAt1CCHQ6HaIoIoRAtxACnU6HWBRFhBDoFkKg0+nwfgkhcCdXr16lUqnw8Y9/nMuXL/OjH/2I73//+ywuLhKLoogQAt1CCHQ6HaIoIoRAtxACnU6HWBRFhBDoFkKg0+mwkQTcH+nt7SWKIrp1Oh16enrYqH7xi1/whS98gS996UtkMhlivb29RFFEt06nQ09PD7He3l6iKKJbp9Ohp6eH3t5eoiiiW6fToaenh3vlxRdfZO/evbz99tvUajXW19f55S9/iSR6e3uJoohunU6Hnp4eYr29vURRRLdOp0NPTw+9vb1EUUS3TqdDT08P98JHP/pRHnnkEZ5++mliZsaTTz7Jq6++Sm9vL1EU0a3T6dDT00Ost7eXKIro1ul06Onpobe3lyiK6NbpdOjp6eFeeP311/nHf/xHzpw5wzPPPMPp06eJ/d3f/R29vb1EUUS3TqdDT08Psd7eXqIoolun06Gnp4fe3l6iKKJbp9Ohp6eHe623t5coiujW6XTo6ekh1tvbSxRFdOt0OvT09NDb20sURXTrdDr09PQQ6+3tJYoiunU6HXp6eriXfvGLX/CFL3yBL33pS2QyGW7r7e0liiK6dTodenp66O3tJYoiunU6HXp6eoj19vYSRRHdOp0OPT093G0vvvgie/fu5e2336ZWq7G+vs4vf/lLJBHr7e0liiK6dTodenp66O3tJYoiunU6HXp6eoj19vYSRRHdOp0OPT093G0f/ehHeeSRR3j66aeJmRlPPvkkr776KrHe3l6iKKJbp9Ohp6eH3t5eoiiiW6fToaenh1hvby9RFNGt0+nQ09PDRhJwf2RwcJCVlRW6tVotRkZG2Ijq9Tpf/vKX+fa3v83hw4e5bXBwkJWVFbq1Wi1GRkaIDQ4OsrKyQrdWq8XIyAiDg4OsrKzQrdVqMTIywr3yoQ99iHfeeYczZ85w5swZfvWrX1Gr1ajX6wwODrKyskK3VqvFyMgIscHBQVZWVujWarUYGRlhcHCQlZUVurVaLUZGRrgXPvjBD/JuIQRCCAwODrKyskK3VqvFyMgIscHBQVZWVujWarUYGRlhcHCQlZUVurVaLUZGRrgXWq0WH/vYx3jooYe47ZFHHqHZbDI4OMjKygrdWq0WIyMjxAYHB1lZWaFbq9ViZGSEwcFBVlZW6NZqtRgZGeFeGxwcZGVlhW6tVouRkRFig4ODrKys0K3VajEyMsLg4CArKyt0a7VajIyMEBscHGRlZYVurVaLkZER7pV6vc6Xv/xlvv3tb3P48GG6DQ4OsrKyQrdWq8XIyAiDg4OsrKzQrdVqMTIyQmxwcJCVlRW6tVotRkZGuNs+9KEP8c4773DmzBnOnDnDr371K2q1GvV6ndjg4CArKyt0a7VajIyMMDg4yMrKCt1arRYjIyPEBgcHWVlZoVur1WJkZIS77YMf/CDvFkIghEBscHCQlZUVurVaLUZGRhgcHGRlZYVurVaLkZERYoODg6ysrNCt1WoxMjLCRhJwfySVShGr1WrE1tbWqNfrjI2NsdE0m02+9rWv8eKLL3LgwAFu3brFrVu3iKKIVCpFrFarEVtbW6NerzM2NkYslUoRq9VqxNbW1qjX64yNjZFKpYjVajVia2tr1Ot1xsbGuFe+8Y1vcPr0aU6fPs3p06f5xCc+wZEjRzh8+DCpVIpYrVYjtra2Rr1eZ2xsjFgqlSJWq9WIra2tUa/XGRsbI5VKEavVasTW1tao1+uMjY1xLxw4cID19XXOnTtHbH19nQsXLvD5z3+eVCpFrFarEVtbW6NerzM2NkYslUoRq9VqxNbW1qjX64yNjZFKpYjVajVia2tr1Ot1xsbGuBf27t3L0tIS//RP/0Tsd7/7HW+++SaPPvooqVSKWK1WI7a2tka9XmdsbIxYKpUiVqvViK2trVGv1xkbGyOVShGr1WrE1tbWqNfrjI2Nca+lUilitVqN2NraGvV6nbGxMWKpVIpYrVYjtra2Rr1eZ2xsjFQqRaxWqxFbW1ujXq8zNjZGLJVKEavVasTW1tao1+uMjY1xLzSbTb72ta/x4osvcuDAAW7dusWtW7eIoohYKpUiVqvViK2trVGv1xkbGyOVShGr1WrE1tbWqNfrjI2NEUulUsRqtRqxtbU16vU6Y2Nj3G3f+MY3OH36NKdPn+b06dN84hOf4MiRIxw+fJhYKpUiVqvViK2trVGv1xkbGyOVShGr1WrE1tbWqNfrjI2NEUulUsRqtRqxtbU16vU6Y2Nj3G0HDhxgfX2dc+fOEVtfX+fChQt8/vOfJ5ZKpYjVajVia2tr1Ot1xsbGSKVSxGq1GrG1tTXq9TpjY2PEUqkUsVqtRmxtbY16vc7Y2BgbScD9kRAChUKBbDbL1NQUk5OTzMzMMDAwwEZz5swZ3nnnHb761a+yb98+9u3bx759+/je975HCIFCoUA2m2VqaorJyUlmZmYYGBggFkKgUCiQzWaZmppicnKSmZkZBgYGCCFQKBTIZrNMTU0xOTnJzMwMAwMDbAQhBAqFAtlslqmpKSYnJ5mZmWFgYIBYCIFCoUA2m2VqaorJyUlmZmYYGBgghEChUCCbzTI1NcXk5CQzMzMMDAxwL3zgAx/g5MmTfOc73yGdTvPUU0/x9NNPMzo6SgiBQqFANptlamqKyclJZmZmGBgYIBZCoFAokM1mmZqaYnJykpmZGQYGBgghUCgUyGazTE1NMTk5yczMDAMDA9wLe/bsIZvN8pd/+ZdMTU3x6U9/mr/4i7/gf/7P/0kIgUKhQDabZWpqisnJSWZmZhgYGCAWQqBQKJDNZpmammJycpKZmRkGBgYIIVAoFMhms0xNTTE5OcnMzAwDAwPcayEECoUC2WyWqakpJicnmZmZYWBggFgIgUKhQDabZWpqisnJSWZmZhgYGCCEQKFQIJvNMjU1xeTkJDMzMwwMDBALIVAoFMhms0xNTTE5OcnMzAwDAwPcC2fOnOGdd97hq1/9Kvv27WPfvn3s27eP733ve8RCCBQKBbLZLFNTU0xOTjIzM8PAwAAhBAqFAtlslqmpKSYnJ5mZmWFgYIBYCIFCoUA2m2VqaorJyUlmZmYYGBjgfgshUCgUyGazTE1NMTk5yczMDAMDA4QQKBQKZLNZpqammJycZGZmhoGBAWIhBAqFAtlslqmpKSYnJ5mZmWFgYIC77QMf+AAnT57kO9/5Dul0mqeeeoqnn36a0dFRYiEECoUC2WyWqakpJicnmZmZYWBggBAChUKBbDbL1NQUk5OTzMzMMDAwQCyEQKFQIJvNMjU1xeTkJDMzMwwMDLCRBNwdjY6OsrS0xNzcHMvLy0xMTLARfetb30ISkpCEJCRx7NgxYqOjoywtLTE3N8fy8jITExN0Gx0dZWlpibm5OZaXl5mYmOC20dFRlpaWmJubY3l5mYmJCe6n06dP89nPfpbbRkdHWVpaYm5ujuXlZSYmJug2OjrK0tISc3NzLC8vMzExwW2jo6MsLS0xNzfH8vIyExMT3Euf+tSnOHfuHMVikTfeeIMjR45w2+joKEtLS8zNzbG8vMzExATdRkdHWVpaYm5ujuXlZSYmJrhtdHSUpaUl5ubmWF5eZmJignvp6aefZnl5mbm5Od544w2OHj3KbaOjoywtLTE3N8fy8jITExN0Gx0dZWlpibm5OZaXl5mYmOC20dFRlpaWmJubY3l5mYmJCe6Fxx9/nAsXLtBtdHSUpaUl5ubmWF5eZmJigm6jo6MsLS0xNzfH8vIyExMT3DY6OsrS0hJzc3MsLy8zMTFBt9HRUZaWlpibm2N5eZmJiQnulscff5wLFy5w27e+9S0kIQlJSEISx44d47bR0VGWlpaYm5tjeXmZiYkJbhsdHWVpaYm5uTmWl5eZmJig2+joKEtLS8zNzbG8vMzExAR3w+OPP86FCxd4L6dPn+azn/0s3UZHR1laWmJubo7l5WUmJia4bXR0lKWlJebm5lheXmZiYoJuo6OjLC0tMTc3x/LyMhMTE9wNjz/+OBcuXKDbpz71Kc6dO0exWOSNN97gyJEjdBsdHWVpaYm5uTmWl5eZmJjgttHRUZaWlpibm2N5eZmJiQm6jY6OsrS0xNzcHMvLy0xMTLDRBNy/q6+vjxACm11fXx8hBN5LX18fIQTupK+vjxACG1VfXx8hBN5LX18fIQTupK+vjxAC98v27dt56KGHuJO+vj5CCLyXvr4+QgjcSV9fHyEE7ocQAn19fTz00EPcSV9fHyEE3ktfXx8hBO6kr6+PEAIbQV9fHyEE3ktfXx8hBO6kr6+PEALvpa+vjxACG1VfXx8hBO6kr6+PEALvpa+vjxACG1FfXx8hBO6kr6+PEALvpa+vjxAC98P27dt56KGHeC99fX2EELiTvr4+Qgi8l76+PkIIbEQB55xzzrlNJuCcc845t8kEnHPOOec2mYBzzjnn3CYTcM4555zbZALOOeecc5tMwDnnnHNukwk455xzzm0yAeecc865TSbgnHPOObfJBJxzzjnnNpmAc84559wmE3DOOeec22QCzjnnnHObTMA555xzbpMJOOecc85tMgHnnHPOuU0m4Jxzzjm3yQScc8455zaZgHPOOefcJhNwzjnnnNtkAs4555xzm0zAOeecc26TCTjnnHPObTIB55xzzrlN5v8F2D8taHjotFwAAAAASUVORK5CYII=" style="width: 100%; height: auto;"></div></div></div></div></div></div><div class="inlineWrapper outputs"><div class = 'S5'><span style="white-space: pre;"><span>disp(win(1:10));</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="591A4ECB" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="213" data-hashorizontaloverflow="false" data-testid="output_2" style="max-height: 261px; width: 1218px;"><div class="textElement"> 1 ~ 3번 열
0.080000000000000 0.080000035473324 0.080000141893292
4 ~ 6번 열
0.080000319259887 0.080000567573081 0.080000886832836
7 ~ 9번 열
0.080001277039103 0.080001738191823 0.080002270290922
10번 열
0.080002873336321</div></div></div></div></div><h2 class = 'S4'><span>testing calculating xcorr</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S0'><span style="white-space: pre;"><span>npiece = 200;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>nfft = 16384*2;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>nbest = 2;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>nmask = 5;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>scroll = floor(nsample / (npiece+2)); </span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>stft1 = fft([x(1,scroll+1:(scroll+nwin)) .* win, zeros(1,nfft-nwin)]); </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>stft2 = fft([x(2,scroll+1:(scroll+nwin)) .* win, zeros(1,nfft-nwin)]); </span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>numerator = stft1 .* conj(stft2);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>ccorr = real(ifft(numerator ./ (abs(numerator))));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>ccorr = [ccorr(end-479:end), ccorr(1:480)];</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>[best_ccorr, best_idx] = maxk(ccorr, nbest, nmask);</span></span></div></div><div class="inlineWrapper outputs"><div class = 'S2'><span style="white-space: pre;"><span>disp(scroll);</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="BFEEC173" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="18" data-hashorizontaloverflow="false" data-testid="output_3" style="max-height: 261px; width: 1218px;"><div class="textElement"> 290</div></div></div></div><div class="inlineWrapper outputs"><div class = 'S5'><span style="white-space: pre;"><span>plot(ccorr);</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsFigure" uid="E48DDEDE" data-scroll-top="null" data-scroll-left="null" data-testid="output_4" style="width: 1218px;"><div class="figureElement"><div class="figureContainingNode" style="width: 560px; max-width: 100%; display: inline-block;"><div class="GraphicsView" data-dojo-attach-point="graphicsViewNode,backgroundColorNode" id="uniqName_197_84" widgetid="uniqName_197_84" style="width: 100%; height: auto;"><img class="ImageView figureImage" data-dojo-attach-point="imageViewNode" draggable="false" ondragstart="return false;" id="uniqName_197_86" widgetid="uniqName_197_86" 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" style="width: 100%; height: auto;"></div></div></div></div></div></div><div class="inlineWrapper"><div class = 'S6'></div></div><div class="inlineWrapper outputs"><div class = 'S2'><span style="white-space: pre;"><span>disp(best_ccorr);</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="59831588" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="31" data-hashorizontaloverflow="false" data-testid="output_5" style="max-height: 261px; width: 1218px;"><div class="textElement"> 0.032781450919252
0.029635834307374</div></div></div></div></div><h2 class = 'S4'><span>calculate avg_ccorr</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S0'><span style="white-space: pre;"><span>avg_ccorr = zeros(nmic, nmic);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>i = 1:npiece</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> st = i * scroll + 1;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> ed = st + 16000 - 1;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">if </span><span>st + 16384 >= nsample</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">break</span><span>;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>m1 = 1:(nmic-1)</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> avg_ccorr(m1, m1) = 0;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>m2 = (m1+1):nmic</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> stft1 = fft([x(m1,st:ed) .* win, zeros(1,nfft-nwin)]); </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> stft2 = fft([x(m2,st:ed) .* win, zeros(1,nfft-nwin)]);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> numerator = stft1 .* conj(stft2);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> ccorr = real(ifft(numerator ./ (abs(numerator))));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> ccorr = [ccorr(end-479:end), ccorr(1:480)];</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> maxk_val = sum(maxk(ccorr, nbest, nmask));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> avg_ccorr(m1, m2) = avg_ccorr(m1, m2) + (maxk_val);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> avg_ccorr(m2, m1) = avg_ccorr(m1, m2); </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>avg_ccorr = avg_ccorr / (nbest * npiece);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper outputs"><div class = 'S2'><span style="white-space: pre;"><span>disp(avg_ccorr);</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="13CE0345" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="213" data-hashorizontaloverflow="false" data-testid="output_6" style="max-height: 261px; width: 1218px;"><div class="textElement"> 1 ~ 3번 열
0 0.022342359302499 0.038002415483785
0.022342359302499 0 0.078103387449447
0.038002415483785 0.078103387449447 0
0.159321275205544 0.021976501561010 0.038497480480202
0.116734197033740 0.022246232748426 0.034219510992536
4 ~ 5번 열
0.159321275205544 0.116734197033740
0.021976501561010 0.022246232748426
0.038497480480202 0.034219510992536
0 0.153034492978611
0.153034492978611 0</div></div></div></div><div class="inlineWrapper outputs"><div class = 'S5'><span style="white-space: pre;"><span>fprintf(</span><span style="color: rgb(160, 32, 240);">'%.8f\n'</span><span>, sum(avg_ccorr/nmic));</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="6A5975E1" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="73" data-hashorizontaloverflow="false" data-testid="output_7" style="max-height: 261px; width: 1218px;"><div class="textElement">0.06728005
0.02893370
0.03776456
0.07456595
0.06524689</div></div></div></div><div class="inlineWrapper outputs"><div class = 'S5'><span style="white-space: pre;"><span>disp(sum(avg_ccorr,1));</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="AF5046EF" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="101" data-hashorizontaloverflow="false" data-testid="output_8" style="max-height: 261px; width: 1218px;"><div class="textElement"> 1 ~ 3번 열
0.336400247025568 0.144668481061382 0.188822794405971
4 ~ 5번 열
0.372829750225366 0.326234433753313</div></div></div></div><div class="inlineWrapper outputs"><div class = 'S5'><span style="white-space: pre;"><span>disp(sum(avg_ccorr,1)/nmic);</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="813A8840" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="101" data-hashorizontaloverflow="false" data-testid="output_9" style="max-height: 261px; width: 1218px;"><div class="textElement"> 1 ~ 3번 열
0.067280049405114 0.028933696212276 0.037764558881194
4 ~ 5번 열
0.074565950045073 0.065246886750663</div></div></div></div><div class="inlineWrapper"><div class = 'S6'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>[dummy, ref_mic] = max(sum(avg_ccorr));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper outputs"><div class = 'S2'><span style="white-space: pre;"><span>disp(ref_mic); </span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="0B8EEB17" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="18" data-hashorizontaloverflow="false" data-testid="output_10" style="max-height: 261px; width: 1218px;"><div class="textElement"> 4</div></div></div></div></div><h2 class = 'S4'><span>calculating scaling factor</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S0'><span style="white-space: pre;"><span>nsegment = 10;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>max_val = zeros(nmic, 1);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">if </span><span>size(x,2) <= 160000 </span><span style="color: rgb(34, 139, 34);">% 10 seconds</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>m = 1:nmic</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> max_val(m) = max(abs(x(m,:)));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">else</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">if </span><span>size(x,2) < 1600000 </span><span style="color: rgb(34, 139, 34);">% 100 seconds</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> nsegment = floor(size(x,2) / 160000);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> scroll = floor(size(x,2) / nsegment);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> max_val_candidate = zeros(nmic, nsegment);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>s = 0:(nsegment-1)</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> st = s * scroll + 1;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> ed = st + 160000 - 1;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>m = 1:nmic</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> max_val_candidate(m,s+1) = max(abs(x(m,st:ed)));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>m = 1:nmic</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> sorted = sort(max_val_candidate(m,:), </span><span style="color: rgb(160, 32, 240);">'ascend'</span><span>);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">if </span><span>length(sorted(:)) > 2</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> max_val(m) = sorted(end/2 + 1);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">else</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> max_val(m) = sorted;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>overall_weight = (0.3 * nmic) / sum(max_val);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper outputs"><div class = 'S2'><span style="white-space: pre;"><span>disp(max_val');</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="C4048E4E" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="101" data-hashorizontaloverflow="false" data-testid="output_11" style="max-height: 261px; width: 1218px;"><div class="textElement"> 1 ~ 3번 열
0.228759765625000 1.000000000000000 0.025970458984375
4 ~ 5번 열
0.115905761718750 0.138183593750000</div></div></div></div><div class="inlineWrapper outputs"><div class = 'S5'><span style="white-space: pre;"><span>disp(overall_weight);</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="383DA1F7" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="18" data-hashorizontaloverflow="false" data-testid="output_12" style="max-height: 261px; width: 1218px;"><div class="textElement"> 0.994154648975547</div></div></div></div></div><h2 class = 'S4'><span>compute total number of delays</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S0'><span style="white-space: pre;"><span style="color: rgb(34, 139, 34);">% int totalNumDelays</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(34, 139, 34);">% % = (int)((m_frames - (*m_config).windowFrames - m_biggestSkew - m_UEMGap)/((*m_config).</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(34, 139, 34);">% rate*m_sampleRateInMs));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(34, 139, 34);">% sr_in_ms = 16000 / 1000; % 16</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(34, 139, 34);">% too complicated. I should do hard coding</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>nframe = floor(( nsample - 8000 ) / (4000));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper outputs"><div class = 'S2'><span style="white-space: pre;"><span>disp(nframe);</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="EF210263" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="18" data-hashorizontaloverflow="false" data-testid="output_13" style="max-height: 261px; width: 1218px;"><div class="textElement"> 12</div></div></div></div></div><h2 class = 'S4'><span>recreating hamming window</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S0'><span style="white-space: pre;"><span>nwin = 8000; </span><span style="color: rgb(34, 139, 34);">% 0.5 sec</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>nfft = 16384;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>win = zeros(1,nwin);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>i = 1:nwin</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> win(i) = 0.54 - 0.46 * cos(6.283185307*(i-1)/(nwin-1));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper outputs"><div class = 'S2'><span style="white-space: pre;"><span>figure; plot(win);</span></span></div><div class = 'S3'><div 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" style="width: 100%; height: auto;"></div></div></div></div></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre;"><span style="color: rgb(34, 139, 34);">% Marginst for delays in frames: 480 in ms: 30</span></span></div></div><div class="inlineWrapper outputs"><div class = 'S2'><span style="white-space: pre;"><span>disp(16000 * 30 / 1000);</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="2F10E359" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="18" data-hashorizontaloverflow="false" data-testid="output_15" style="max-height: 261px; width: 1218px;"><div class="textElement"> 480</div></div></div></div></div><h2 class = 'S7'><span>compute TDOA</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S0'><span style="white-space: pre;"><span>nbest = 4;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>micpair = zeros(nmic, npair);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>gcc_nbest = zeros(npair, nframe, nbest);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>tdoa_nbest = zeros(npair, nframe, nbest);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>lrfilp_gcc = zeros(npair, nframe, nfft);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>m = 1:nmic</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> p = 1; </span><span style="color: rgb(34, 139, 34);">% pair idx</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>i = 1:nmic</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> micpair(m,p) = i;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> p = p + 1;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>t = 1:(nframe)</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> st = (t-1) * 4000 + 1;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> ed = st + nwin - 1;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>p = 1:npair</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> m = micpair(ref_mic,p);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> stft_ref = fft([x(ref_mic,st:ed) .* win, zeros(1,nfft-nwin)]); </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> stft_m = fft([x(m,st:ed) .* win, zeros(1,nfft-nwin)]);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> numerator = stft_m .* conj(stft_ref);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> gcc = real(ifft(numerator ./ (eps+abs(numerator))));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> gcc = [gcc(end-479:end), gcc(1:480)];</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> [gcck, tdoak] = maxk(gcc, nbest, nmask);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> gcc_nbest(p,t,:) = gcck;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> tdoa_nbest(p,t,:) = tdoak;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> tdoa_nbest(p,t,:) = tdoa_nbest(p,t,:) - (481); </span><span style="color: rgb(34, 139, 34);">% index shifting</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper outputs"><div class = 'S2'><span style="white-space: pre;"><span>disp(squeeze(gcc_nbest(:,:,1)));</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="76919FB4" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="437" data-hashorizontaloverflow="false" data-testid="output_16" style="max-height: 448px; width: 1218px;"><div class="textElement"> 1 ~ 3번 열
0.134777527147523 0.310714006511790 0.314917088716397
0.038170132109127 0.038882672962859 0.040929068935400
0.036834531567438 0.070516117486481 0.065935288669931
0.999999999981588 0.999999999988105 0.999999999914246
0.172777734235956 0.241398231239069 0.262939287365895
4 ~ 6번 열
0.219783203852808 0.305739643099305 0.205989675419255
0.040529452208426 0.040242533837290 0.038297009643333
0.058450959230501 0.047566424970544 0.043482510827778
0.999999999989013 0.999999999980583 0.999999999942182
0.214451989280496 0.220126884800238 0.204886564654973
7 ~ 9번 열
0.139442190097295 0.247661604455948 0.235643518139056
0.045184767676717 0.040162493825616 0.046668417418980
0.068271058129833 0.080619738420894 0.084556027188915
0.999999999973040 0.999999999993713 0.999999999028075
0.207187769957736 0.253262170513861 0.268108220115873
10 ~ 12번 열
0.169198224556999 0.128140412249901 0.229560206099731
0.039060692331839 0.051601605700296 0.054813524162973
0.059109809721432 0.052225529852137 0.094269824326320
0.999999999964877 0.999999999972500 0.999999999951030
0.214279085500231 0.161035187225036 0.339287338501401</div></div></div></div><div class="inlineWrapper outputs"><div class = 'S5'><span style="white-space: pre;"><span>disp(squeeze(gcc_nbest(:,:,2)));</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="20FC7EFF" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="437" data-hashorizontaloverflow="false" data-testid="output_17" style="max-height: 448px; width: 1218px;"><div class="textElement"> 1 ~ 3번 열
0.114963745017336 0.195475855758566 0.178648556747342
0.035116742147854 0.037100053263609 0.035637360007886
0.035679446602257 0.055503548679856 0.061724224597425
0.000000000017548 0.000000000011468 0.000000000083928
0.133339519236832 0.200525040686099 0.241082840169924
4 ~ 6번 열
0.158157287770155 0.260599768920549 0.205012091175727
0.038385342961917 0.039096335915366 0.034038067625336
0.050090393606936 0.047073220807878 0.037751163946570
0.000000000010134 0.000000000018172 0.000000000053912
0.166791715526935 0.208421232232020 0.202540786009772
7 ~ 9번 열
0.138911452036715 0.223651054016421 0.231282995204979
0.044228359122572 0.036658221849904 0.045221332635653
0.053995335553000 0.042618306301619 0.076562882452097
0.000000000026109 0.000000000005186 0.000000000965135
0.145884405215472 0.195681079491320 0.188905560986978
10 ~ 12번 열
0.158667153198419 0.101685282275355 0.201039993725824
0.037017467677507 0.050047621913277 0.054795654141421
0.052497822553792 0.049640324058591 0.078862713646111
0.000000000032021 0.000000000026908 0.000000000048080
0.161872095087534 0.154554751870244 0.173282927356838</div></div></div></div><div class="inlineWrapper"><div class = 'S6'></div></div><div class="inlineWrapper outputs"><div class = 'S2'><span style="white-space: pre;"><span>disp(squeeze(tdoa_nbest(:,:,1)));</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="92B0F7B6" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="73" data-hashorizontaloverflow="false" data-testid="output_18" style="max-height: 261px; width: 1218px;"><div class="textElement"> -6 0 0 0 0 -5 -5 0 -5 -5 -8 0
267 4 -464 -204 416 101 -75 -367 -354 175 393 -383
-283 -1 -3 -316 39 -1 -5 -1 7 -1 -101 3
0 0 0 0 0 0 0 0 0 0 0 0
2 -4 -4 2 -4 2 2 2 2 2 -4 4</div></div></div></div><div class="inlineWrapper outputs"><div class = 'S5'><span style="white-space: pre;"><span>disp(squeeze(tdoa_nbest(:,:,2)));</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="87AABD14" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="73" data-hashorizontaloverflow="false" data-testid="output_19" style="max-height: 261px; width: 1218px;"><div class="textElement"> 0 -5 -8 -5 -5 0 0 -5 0 0 0 -5
475 -33 365 -114 389 -265 15 242 290 266 191 232
68 -6 5 -5 -1 318 -27 -122 -1 7 -222 -5
5 -5 -5 5 -5 -5 5 -5 -5 5 5 5
-4 1 4 -4 2 -4 -4 -3 -3 -4 4 -1</div></div></div></div></div><h2 class = 'S4'><span>find noise threshold</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S0'><span style="white-space: pre;"><span>th_idx = floor((0.1 * nframe)) + 1;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>sorted = sort(sum(gcc_nbest(:,:,1),1) - sum(gcc_nbest(ref_mic,:,1),1), </span><span style="color: rgb(160, 32, 240);">'ascend'</span><span>);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>threshold = sorted(th_idx)/(nmic-1);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(34, 139, 34);">% Computing the optimum delays</span></span></div></div><div class="inlineWrapper outputs"><div class = 'S2'><span style="white-space: pre;"><span>disp(threshold);</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="32E193AF" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="18" data-hashorizontaloverflow="false" data-testid="output_20" style="max-height: 261px; width: 1218px;"><div class="textElement"> 0.098250683756843</div></div></div></div></div><h2 class = 'S4'><span>noise filtering</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S0'><span style="white-space: pre;"><span>noise_filter = zeros(npair, nframe);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>p = 1:npair</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>t = 1:nframe</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">if </span><span>gcc_nbest(p,t,1) < threshold</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> noise_filter(p,t) = 1;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">if </span><span>t == 1 </span><span style="color: rgb(34, 139, 34);">% it's silence</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> gcc_nbest(p,t,:) = 0; </span><span style="color: rgb(34, 139, 34);">% masking with discouraging values</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> gcc_nbest(p,t,1) = 1; </span><span style="color: rgb(34, 139, 34);">% only one path</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> tdoa_nbest(p,t,:) = 480; </span><span style="color: rgb(34, 139, 34);">% masking with discouraging values</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> tdoa_nbest(p,t,1) = 0;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">else</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> tdoa_nbest(p,t,:) = tdoa_nbest(p,t-1,:);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">if </span><span>p == ref_mic</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> gcc_nbest(p,t,:) = 0; </span><span style="color: rgb(34, 139, 34);">% masking with discouraging values</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> gcc_nbest(p,t,1) = 1; </span><span style="color: rgb(34, 139, 34);">% only one path</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> tdoa_nbest(p,t,:) = 0; </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper outputs"><div class = 'S2'><span style="white-space: pre;"><span>disp(gcc_nbest(:,:,1));</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="7E0514E3" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="437" data-hashorizontaloverflow="false" data-testid="output_21" style="max-height: 448px; width: 1218px;"><div class="textElement"> 1 ~ 3번 열
0.134777527147523 0.310714006511790 0.314917088716397
1.000000000000000 0.038882672962859 0.040929068935400
1.000000000000000 0.070516117486481 0.065935288669931
1.000000000000000 1.000000000000000 1.000000000000000
0.172777734235956 0.241398231239069 0.262939287365895
4 ~ 6번 열
0.219783203852808 0.305739643099305 0.205989675419255
0.040529452208426 0.040242533837290 0.038297009643333
0.058450959230501 0.047566424970544 0.043482510827778
1.000000000000000 1.000000000000000 1.000000000000000
0.214451989280496 0.220126884800238 0.204886564654973
7 ~ 9번 열
0.139442190097295 0.247661604455948 0.235643518139056
0.045184767676717 0.040162493825616 0.046668417418980
0.068271058129833 0.080619738420894 0.084556027188915
1.000000000000000 1.000000000000000 1.000000000000000
0.207187769957736 0.253262170513861 0.268108220115873
10 ~ 12번 열
0.169198224556999 0.128140412249901 0.229560206099731
0.039060692331839 0.051601605700296 0.054813524162973
0.059109809721432 0.052225529852137 0.094269824326320
1.000000000000000 1.000000000000000 1.000000000000000
0.214279085500231 0.161035187225036 0.339287338501401</div></div></div></div><div class="inlineWrapper outputs"><div class = 'S5'><span style="white-space: pre;"><span>disp(tdoa_nbest(:,:,1));</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="CF01CB71" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="73" data-hashorizontaloverflow="false" data-testid="output_22" style="max-height: 261px; width: 1218px;"><div class="textElement"> -6 0 0 0 0 -5 -5 0 -5 -5 -8 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
2 -4 -4 2 -4 2 2 2 2 2 -4 4</div></div></div></div><div class="inlineWrapper"><div class = 'S6'></div></div><div class="inlineWrapper outputs"><div class = 'S2'><span style="white-space: pre;"><span>disp(gcc_nbest(:,:,2));</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="D5E7D4B7" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="437" data-hashorizontaloverflow="false" data-testid="output_23" style="max-height: 448px; width: 1218px;"><div class="textElement"> 1 ~ 3번 열
0.114963745017336 0.195475855758566 0.178648556747342
0 0.037100053263609 0.035637360007886
0 0.055503548679856 0.061724224597425
0 0 0
0.133339519236832 0.200525040686099 0.241082840169924
4 ~ 6번 열
0.158157287770155 0.260599768920549 0.205012091175727
0.038385342961917 0.039096335915366 0.034038067625336
0.050090393606936 0.047073220807878 0.037751163946570
0 0 0
0.166791715526935 0.208421232232020 0.202540786009772
7 ~ 9번 열
0.138911452036715 0.223651054016421 0.231282995204979
0.044228359122572 0.036658221849904 0.045221332635653
0.053995335553000 0.042618306301619 0.076562882452097
0 0 0
0.145884405215472 0.195681079491320 0.188905560986978
10 ~ 12번 열
0.158667153198419 0.101685282275355 0.201039993725824
0.037017467677507 0.050047621913277 0.054795654141421
0.052497822553792 0.049640324058591 0.078862713646111
0 0 0
0.161872095087534 0.154554751870244 0.173282927356838</div></div></div></div><div class="inlineWrapper outputs"><div class = 'S5'><span style="white-space: pre;"><span>disp(tdoa_nbest(:,:,2));</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="E1D6EE5A" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="73" data-hashorizontaloverflow="false" data-testid="output_24" style="max-height: 261px; width: 1218px;"><div class="textElement"> 0 -5 -8 -5 -5 0 0 -5 0 0 0 -5
480 480 480 480 480 480 480 480 480 480 480 480
480 480 480 480 480 480 480 480 480 480 480 480
0 0 0 0 0 0 0 0 0 0 0 0
-4 1 4 -4 2 -4 -4 -3 -3 -4 4 -1</div></div></div></div></div><h2 class = 'S4'><span>single channel viterbi - emission, trans prob</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S0'><span style="white-space: pre;"><span>emission1 = zeros(npair, nframe, nbest);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>diff1 = zeros(npair, nframe, nbest, nbest); </span><span style="color: rgb(34, 139, 34);">% do not using 1st idx</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>transition1 = zeros(npair, nframe, nbest, nbest); </span><span style="color: rgb(34, 139, 34);">% do not using 1st idx</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>p = 1:npair</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>t = 1:nframe</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>n = 1:nbest </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">if </span><span>gcc_nbest(p,t,n) <= 0</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> emission1(p,t,n) = -1000;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">else</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> emission1(p,t,n) = log10(gcc_nbest(p,t,n) / sum(gcc_nbest(p,t,:)));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>p = 1:npair</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>t = 2:nframe</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>n = 1:nbest</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>nprev = 1:nbest</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> diff1(p,t,n,nprev) = abs(tdoa_nbest(p,t,n) - tdoa_nbest(p,t-1,nprev));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>p = 1:npair</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>t = 2:nframe</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>n = 1:nbest</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>nprev = 1:nbest</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(34, 139, 34);">% there is a computational bug.</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(34, 139, 34);">% disp((2+maxdiff1(p,t,n)));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(34, 139, 34);">% disp(diff1(p,t,n,nprev));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(34, 139, 34);">% disp(maxdiff1(p,t,n));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(34, 139, 34);">% disp(log10(481/482));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(34, 139, 34);">% disp(1 + maxdiff1(p,t,n) - diff1(p,t,n,nprev));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(34, 139, 34);">% disp((2+maxdiff1(p,t,n)));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> maxdiff1 = max(diff1(p,t,:));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> nume = (1 + maxdiff1 - diff1(p,t,n,nprev));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> deno = (2 + maxdiff1);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> transition1(p,t,n,nprev) = log(nume / deno) / log(10); </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S8'></div></div></div><h2 class = 'S4'><span>single channel viterbi - searching</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S0'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>nbest2 = 2;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>score1 = zeros(npair, nframe, nbest, nbest); </span><span style="color: rgb(34, 139, 34);">% tmp variable</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>score1_table = zeros(npair, nframe, nbest);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>back1_table = zeros(npair, nframe, nbest);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>bestpath1 = zeros(npair, nframe, nbest2); </span><span style="color: rgb(34, 139, 34);">% state idx stored.</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(34, 139, 34);">% original beamformit</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>dC = ones(npair, nframe, nbest) * -1000;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>tC = ones(npair, nframe, nbest);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>viterbi_score = zeros(npair, 1);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>R = ones(npair, nframe); </span><span style="color: rgb(34, 139, 34);">% best prev state</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>F = ones(npair, nframe);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>forwardTrans = zeros(npair, nframe, nbest);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>selfLoopTrans = zeros(npair, nframe, nbest);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>bestpath1(:,:,1) = 1;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>bestpath1(:,:,2) = 2;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>p = 1:npair</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>n = 1:nbest</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> score1(p,1,n,:) = emission1(p,1,n); </span><span style="color: rgb(34, 139, 34);">% broadcasting</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> score1_table(p,1,n) = emission1(p,1,n);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>p = 1:npair</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>n = 1:nbest</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> dC(p,1,n) = emission1(p,1,n);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>p = 1:npair</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>t = 2:nframe</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>n = 1:nbest</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> best_n_prev = R(p,t-1);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> back1_table(p,t,1) = best_n_prev;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> forwardTrans(p,t,n) = dC(p,t-1,best_n_prev) + 25 * transition1(p,t,n,best_n_prev);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> selfLoopTrans(p,t,n) = dC(p,t-1,n) + 25 * transition1(p,t,n,n);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">if </span><span>selfLoopTrans(p,t,n) >= forwardTrans(p,t,n)</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> dC(p,t,n) = selfLoopTrans(p,t,n) + emission1(p,t,n);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> tC(p,t,n) = tC(p,t-1,n);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">else</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> dC(p,t,n) = forwardTrans(p,t,n) + emission1(p,t,n);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> tC(p,t,n) = t;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> [dummy, R(p,t)] = max(dC(p,t,:));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> F(p,t) = tC(p,t,R(p,t));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> st = F(p,end);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> bestpath1(p,st:end,1) = R(p,end);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">while </span><span>st > 2</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> ed = st - 1;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> st = F(p,ed);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> bestpath1(p,st:ed,1) = R(p,ed);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> viterbi_score(p) = dC(p,end,R(p,end));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper outputs"><div class = 'S2'><span style="white-space: pre;"><span>disp(bestpath1(:,:,1));</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="54C9757E" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="73" data-hashorizontaloverflow="false" data-testid="output_25" style="max-height: 261px; width: 1218px;"><div class="textElement"> 1 1 1 1 1 2 2 1 2 2 2 1
1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 2 1 2 2 2 2 2 1 1</div></div></div></div><div class="inlineWrapper outputs"><div class = 'S5'><span style="white-space: pre;"><span>disp(viterbi_score');</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="D6429652" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="101" data-hashorizontaloverflow="false" data-testid="output_26" style="max-height: 261px; width: 1218px;"><div class="textElement"> 1 ~ 3번 열
-8.177195798314624 -6.512432585836903 -5.781413084413090
4 ~ 5번 열
-82.783248807594831 -10.422622989494176</div></div></div></div></div><h2 class = 'S4'><span>single channel viterbi - 2-best path</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S0'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>gcc_nbest_copy = gcc_nbest;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>emission1_copy = emission1;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>bestpath1(:,:,2) = bestpath1(:,:,1);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>p = 1:npair</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>t = 1:nframe</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> best1 = bestpath1(p,t,1);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> gcc_nbest_copy(p,t,best1) = 0;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>p = 1:npair</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>t = 1:nframe</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>n = 1:nbest </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">if </span><span>gcc_nbest_copy(p,t,n) > 0 </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> emission1_copy(p,t,n) = log10(gcc_nbest_copy(p,t,n) / sum(gcc_nbest_copy(p,t,:)));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">else</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> emission1_copy(p,t,n) = -1000;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>dC = zeros(npair, nframe, nbest);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>tC = ones(npair, nframe, nbest);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>viterbi_score = zeros(npair, 1);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>R = ones(npair, nframe); </span><span style="color: rgb(34, 139, 34);">% best prev state</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>F = ones(npair, nframe);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>forwardTrans = zeros(npair, nframe, nbest);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>selfLoopTrans = zeros(npair, nframe, nbest);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>p = 1:npair</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>n = 1:nbest</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> dC(p,1,n) = emission1_copy(p,1,n);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>p = 1:npair</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>t = 2:nframe</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>n = 1:nbest</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> best_n_prev = R(p,t-1);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> back1_table(p,t,2) = best_n_prev;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> forwardTrans(p,t,n) = dC(p,t-1,best_n_prev) + 25 * transition1(p,t,n,best_n_prev);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> selfLoopTrans(p,t,n) = dC(p,t-1,n) + 25 * transition1(p,t,n,n);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">if </span><span>selfLoopTrans(p,t,n) >= forwardTrans(p,t,n)</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> dC(p,t,n) = selfLoopTrans(p,t,n) + emission1_copy(p,t,n);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> tC(p,t,n) = tC(p,t-1,n);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">else</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> dC(p,t,n) = forwardTrans(p,t,n) + emission1_copy(p,t,n);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> tC(p,t,n) = t;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> [dummy, R(p,t)] = max(dC(p,t,:));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> F(p,t) = tC(p,t,R(p,t));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> st = F(p,end);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> bestpath1(p,st:end,2) = R(p,end);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">while </span><span>st > 2</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> ed = st - 1;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> st = F(p,ed);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> bestpath1(p,st:ed,2) = R(p,ed);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> viterbi_score(p) = dC(p,end,R(p,end));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper outputs"><div class = 'S2'><span style="white-space: pre;"><span>disp(squeeze(bestpath1(1,:,:))');</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="CD4D126D" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="31" data-hashorizontaloverflow="false" data-testid="output_27" style="max-height: 261px; width: 1218px;"><div class="textElement"> 1 1 1 1 1 2 2 1 2 2 2 1
3 3 3 3 3 3 3 3 3 3 3 3</div></div></div></div><div class="inlineWrapper outputs"><div class = 'S5'><span style="white-space: pre;"><span>disp(viterbi_score');</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="80F37B30" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="129" data-hashorizontaloverflow="false" data-testid="output_28" style="max-height: 261px; width: 1218px;"><div class="textElement"> 1.0e+04 *
1 ~ 3번 열
-0.000894918806015 -0.100527863938774 -0.100479105756626
4 ~ 5번 열
-1.208278324880760 -0.000905465152992</div></div></div></div></div><h2 class = 'S4'><span>plot path</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S0'><span style="white-space: pre;"><span style="color: rgb(34, 139, 34);">% https://kr.mathworks.com/help/matlab/ref/scatter.html</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>figure;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>emission_plot = zeros(npair, 3, nbest * nframe);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>p = 1:npair</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>ibest = 1:nbest</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>iframe = 1:nframe</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> emission_plot(p,1,(ibest-1) * nframe + iframe) = iframe;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> emission_plot(p,2,(ibest-1) * nframe + iframe) = ibest;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> emission_plot(p,3,(ibest-1) * nframe + iframe) = 1 + 400 * 10^(emission1(p, iframe, ibest));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>hold </span><span style="color: rgb(160, 32, 240);">on</span><span>;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>p=1:npair</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> subplot(npair,1,p);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> scatter(</span><span style="color: rgb(0, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> squeeze(emission_plot(p, 1,:)), </span><span style="color: rgb(0, 0, 255);">...</span><span style="color: rgb(34, 139, 34);"> % x range (x data for scatter)</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> squeeze(emission_plot(p, 2,:)), </span><span style="color: rgb(0, 0, 255);">...</span><span style="color: rgb(34, 139, 34);"> % y range (y data for scatter)</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> squeeze(emission_plot(p, 3,:)), </span><span style="color: rgb(0, 0, 255);">...</span><span style="color: rgb(34, 139, 34);"> % radius</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(160, 32, 240);">'filled'</span><span>); </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> axis </span><span style="color: rgb(160, 32, 240);">ij</span><span>;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> ylim([0 nbest+1]); </span><span style="color: rgb(34, 139, 34);">% for readability</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>hold </span><span style="color: rgb(160, 32, 240);">on</span><span>;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>max_linewidth = 1+max(max(max(max(transition1))));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>p=1:npair</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> subplot(npair, 1, p);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>t = 2:nframe</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>n = 1:nbest</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>nprev = 1:nbest</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> line([t-1 t], </span><span style="color: rgb(0, 0, 255);">...</span><span style="color: rgb(34, 139, 34);"> % x data</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> [nprev n], </span><span style="color: rgb(0, 0, 255);">...</span><span style="color: rgb(34, 139, 34);"> % y data</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(160, 32, 240);">'LineWidth'</span><span>, 10^(transition1(p,t,n,nprev))*3,</span><span style="color: rgb(0, 0, 255);">...</span><span style="color: rgb(34, 139, 34);"> % tuning</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(160, 32, 240);">'Color'</span><span>,</span><span style="color: rgb(160, 32, 240);">'cyan'</span><span>);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>hold </span><span style="color: rgb(160, 32, 240);">on</span><span>;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>p = 1:npair</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> subplot(npair,1,p);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>t = 2:nframe</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> line([t-1 t], [bestpath1(p,t-1,1) bestpath1(p,t,1)], </span><span style="color: rgb(0, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(160, 32, 240);">'LineWidth'</span><span>, 2, </span><span style="color: rgb(0, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(160, 32, 240);">'Color'</span><span>, </span><span style="color: rgb(160, 32, 240);">'red'</span><span>);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> line([t-1 t], [bestpath1(p,t-1,2) bestpath1(p,t,2)], </span><span style="color: rgb(0, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(160, 32, 240);">'LineWidth'</span><span>, 2, </span><span style="color: rgb(0, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(160, 32, 240);">'Color'</span><span>, </span><span style="color: rgb(160, 32, 240);">'black'</span><span>);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper outputs"><div class = 'S2'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsFigure" uid="21CD4AF9" data-scroll-top="null" data-scroll-left="null" data-testid="output_29" style="width: 1218px;"><div class="figureElement"><div class="figureContainingNode" style="width: 560px; max-width: 100%; display: 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" style="width: 100%; height: auto;"></div></div></div></div></div></div><div class="inlineWrapper"><div class = 'S9'></div></div></div><h2 class = 'S4'><span>multi channel viterbi - state define</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S0'><span style="white-space: pre;"><span>nbest2 = 2;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>nstate = nbest2 ^ npair;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>g = zeros(nstate, npair);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>tmp_row = zeros(3,1);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>l = 1;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>ibest = 1:nbest2</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> [g, l] = fill_all_comb(1, npair, ibest, nbest2, tmp_row, g, l);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper outputs"><div class = 'S2'><span style="white-space: pre;"><span>disp(g);</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="5A91292B" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="451" data-hashorizontaloverflow="false" data-testid="output_30" style="max-height: 462px; width: 1218px;"><div class="textElement"> 1 1 1 1 1
1 1 1 1 2
1 1 1 2 1
1 1 1 2 2
1 1 2 1 1
1 1 2 1 2
1 1 2 2 1
1 1 2 2 2
1 2 1 1 1
1 2 1 1 2
1 2 1 2 1
1 2 1 2 2
1 2 2 1 1
1 2 2 1 2
1 2 2 2 1
1 2 2 2 2
2 1 1 1 1
2 1 1 1 2
2 1 1 2 1
2 1 1 2 2
2 1 2 1 1
2 1 2 1 2
2 1 2 2 1
2 1 2 2 2
2 2 1 1 1
2 2 1 1 2
2 2 1 2 1
2 2 1 2 2
2 2 2 1 1
2 2 2 1 2
2 2 2 2 1
2 2 2 2 2</div></div></div></div></div><h2 class = 'S7'><span>multi channel viterbi - emission, trans prob</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S0'><span style="white-space: pre;"><span>emission2 = zeros(nframe, nstate);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>diff2 = zeros(nmic, nframe, nbest, nbest); </span><span style="color: rgb(34, 139, 34);">% do not using 1st idx</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>transition2 = zeros(nframe, nstate, nstate); </span><span style="color: rgb(34, 139, 34);">% do not using 1st idx</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>t = 1:nframe</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>l = 1:nstate</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>m = 1:npair</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> ibest = bestpath1(m, t, g(l,m));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">if </span><span>gcc_nbest(m,t,ibest) > 0</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> emission2(t, l)</span><span style="color: rgb(0, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> = emission2(t, l)</span><span style="color: rgb(0, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> + log10(gcc_nbest(m,t,ibest));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>t = 2:nframe</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> maxdiff2 = 0;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>ibest = 1:nbest</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>jbest = 1:nbest</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>m = 1:npair-1 </span><span style="color: rgb(34, 139, 34);">% why -1?</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> diff2(m,t,ibest, jbest) = abs(tdoa_nbest(m,t,ibest) - tdoa_nbest(m,t-1,jbest));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">if </span><span>maxdiff2 < diff2(m,t,ibest, jbest)</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> maxdiff2 = diff2(m,t,ibest, jbest);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> diff2(:,t,:,:) = log10( (1 + maxdiff2 - diff2(:,t,:,:)) / (2 + maxdiff2) );</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>l = 1:nstate</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>lprev = 1:nstate</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>m = 1:npair-1 </span><span style="color: rgb(34, 139, 34);">% why -1? </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> ibest = bestpath1(m, t, g(l,m));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> jbest = bestpath1(m, t-1, g(lprev,m));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> transition2(t,l,lprev)</span><span style="color: rgb(0, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> = transition2(t,l,lprev)</span><span style="color: rgb(0, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> + diff2(m,t,ibest,jbest);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper outputs"><div class = 'S2'><span style="white-space: pre;"><span>disp(emission2(2,end));</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="FE579FA8" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="18" data-hashorizontaloverflow="false" data-testid="output_31" style="max-height: 261px; width: 1218px;"><div class="textElement"> -4.530028148174052</div></div></div></div><div class="inlineWrapper outputs"><div class = 'S5'><span style="white-space: pre;"><span>disp(transition2(2,:,1));</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement scrollableOutput" uid="D4D05D9E" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="605" data-hashorizontaloverflow="false" data-testid="output_32" style="max-height: 261px; width: 1218px;"><div class="textElement"> 1 ~ 3번 열
-0.009059314209036 -0.009059314209036 -0.009059314209036
4 ~ 6번 열
-0.009059314209036 -2.691204390582868 -2.691204390582868
7 ~ 9번 열
-2.691204390582868 -2.691204390582868 -2.691204390582868
10 ~ 12번 열
-2.691204390582868 -2.691204390582868 -2.691204390582868
13 ~ 15번 열
-5.373349466956699 -5.373349466956699 -5.373349466956699
16 ~ 18번 열
-5.373349466956699 -0.016436043267791 -0.016436043267791
19 ~ 21번 열
-0.016436043267791 -0.016436043267791 -2.698581119641622
22 ~ 24번 열
-2.698581119641622 -2.698581119641622 -2.698581119641622
25 ~ 27번 열
-2.698581119641622 -2.698581119641622 -2.698581119641622
28 ~ 30번 열
-2.698581119641622 -5.380726196015455 -5.380726196015455
31 ~ 32번 열
-5.380726196015455 -5.380726196015455</div></div></div></div></div><h2 class = 'S7'><span>multi channel viterbi - searching</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S0'><span style="white-space: pre;"><span>back2_table = zeros(nframe, nstate);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>bestpath2 = ones(nframe, 1); </span><span style="color: rgb(34, 139, 34);">% state idx stored.</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>dC = ones(nframe, nstate) * -1000;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>tC = ones(nframe, nstate);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>R = ones(nframe, 1); </span><span style="color: rgb(34, 139, 34);">% best prev state</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>F = ones(nframe, 1);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>forwardTrans = zeros(nframe, nstate);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>selfLoopTrans = zeros(nframe, nstate);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>l = 1:nstate</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> dC(1,l) = emission2(1,l);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>t = 2:nframe</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>l = 1:nstate</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> best_l_prev = R(t-1);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> back2_table(t,l) = best_l_prev;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> forwardTrans(t,l) = dC(t-1,best_l_prev) + 25 * transition2(t,l,best_l_prev);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> selfLoopTrans(t,l) = dC(t-1,l) + 25 * transition2(t,l,l);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">if </span><span>selfLoopTrans(t,l) >= forwardTrans(t,l)</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> dC(t,l) = selfLoopTrans(t,l) + emission2(t,l);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> tC(t,l) = tC(t-1,l);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">else</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> dC(t,l) = forwardTrans(t,l) + emission2(t,l);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> tC(t,l) = t;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> [dummy, R(t)] = max(dC(t,:));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> F(t) = tC(t,R(t));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>st = F(end);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>bestpath2(st:end) = R(end);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">while </span><span>st > 2</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> ed = st - 1;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> st = F(ed);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> bestpath2(st:ed) = R(ed);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>viterbi_score = dC(end,R(end));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>besttdoa = zeros(npair, nframe);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>bestgcc2 = zeros(npair, nframe);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>t = 1:nframe</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>p = 1:npair</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> l = bestpath2(t);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> ibest = bestpath1(p, t, g(l,p));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> besttdoa(p,t) = tdoa_nbest(p,t,ibest);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> bestgcc2(p,t) = gcc_nbest(p,t,ibest);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper outputs"><div class = 'S2'><span style="white-space: pre;"><span>disp(back2_table(:,:)'); </span><span style="color: rgb(34, 139, 34);">% compare first mic pair trellis plot</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="31C31E03" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="451" data-hashorizontaloverflow="false" data-testid="output_33" style="max-height: 462px; width: 1218px;"><div class="textElement"> 0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1
0 1 1 1 2 1 2 2 2 2 2 1</div></div></div></div><div class="inlineWrapper"><div class = 'S6'></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper outputs"><div class = 'S2'><span style="white-space: pre;"><span>disp(besttdoa);</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="10A7DD8B" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="73" data-hashorizontaloverflow="false" data-testid="output_34" style="max-height: 261px; width: 1218px;"><div class="textElement"> -6 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
2 -4 -4 2 -4 2 2 2 2 2 -4 4</div></div></div></div></div><h2 class = 'S4'><span>multi channel viterbi - plot</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S0'><span style="white-space: pre;"><span style="color: rgb(34, 139, 34);">% https://kr.mathworks.com/help/matlab/ref/scatter.html</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>figure;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>title(sprintf(</span><span style="color: rgb(160, 32, 240);">'step 2 emission2, transition2'</span><span>));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>t = 2:nframe</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>l = 1:nstate</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>lprev = 1:nstate</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> line([t-1 t], </span><span style="color: rgb(0, 0, 255);">...</span><span style="color: rgb(34, 139, 34);"> % x data</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> [lprev l], </span><span style="color: rgb(0, 0, 255);">...</span><span style="color: rgb(34, 139, 34);"> % y data</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(160, 32, 240);">'LineWidth'</span><span>, 3*(exp(transition2(t,l,lprev))),</span><span style="color: rgb(0, 0, 255);">...</span><span style="color: rgb(34, 139, 34);"> % tuning</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(160, 32, 240);">'Color'</span><span>,</span><span style="color: rgb(160, 32, 240);">'cyan'</span><span>);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> axis </span><span style="color: rgb(160, 32, 240);">ij</span><span>;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>emission2_plot = zeros(3, nstate * nframe);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>l = 1:nstate</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>iframe = 1:nframe</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> emission2_plot(1,(l-1) * nframe + iframe) = iframe;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> emission2_plot(2,(l-1) * nframe + iframe) = l;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> emission2_plot(3,(l-1) * nframe + iframe) = 1 + 400 * 10^(emission2(iframe, l));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>hold </span><span style="color: rgb(160, 32, 240);">on</span><span>;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>scatter(</span><span style="color: rgb(0, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> squeeze(emission2_plot(1,:)), </span><span style="color: rgb(0, 0, 255);">...</span><span style="color: rgb(34, 139, 34);"> % x range (x data for scatter)</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> squeeze(emission2_plot(2,:)), </span><span style="color: rgb(0, 0, 255);">...</span><span style="color: rgb(34, 139, 34);"> % y range (y data for scatter)</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> squeeze(emission2_plot(3,:)), </span><span style="color: rgb(0, 0, 255);">...</span><span style="color: rgb(34, 139, 34);"> % radius</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(160, 32, 240);">'filled'</span><span>);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>hold </span><span style="color: rgb(160, 32, 240);">on</span><span>;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>t = 2:nframe</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> line([t-1 t], [bestpath2(t-1) bestpath2(t)], </span><span style="color: rgb(0, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(160, 32, 240);">'LineWidth'</span><span>, 2, </span><span style="color: rgb(0, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(160, 32, 240);">'Color'</span><span>, </span><span style="color: rgb(160, 32, 240);">'red'</span><span>);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>axis </span><span style="color: rgb(160, 32, 240);">ij</span><span>;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>ylim([0 nstate+1]); </span><span style="color: rgb(34, 139, 34);">% for readability</span></span></div></div><div class="inlineWrapper outputs"><div class = 'S2'><span style="white-space: pre;"><span>xlim([0 nframe+1]);</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsFigure" uid="AA3102C3" data-scroll-top="null" data-scroll-left="null" data-testid="output_35" style="width: 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" style="width: 100%; height: auto;"></div></div></div></div></div></div></div><h2 class = 'S4'><span>compute local xcorr</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S0'><span style="white-space: pre;"><span>tmp_localxcorr = zeros(nmic, nmic, nframe);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span class="warning_squiggle_rte">localxcorr</span><span> = zeros(nmic, nframe);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>mic2refpair = zeros(nmic,1);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>mic2refpair(ref_mic) = 0;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>p = 1:npair</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> m = micpair(ref_mic,p);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> mic2refpair(m) = p;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>t = 1:nframe</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> ref_st = (t-1) * 4000 + 1;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> ref_ed = min(ref_st + 8000 - 1, nsample);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>m1 = 1:(nmic-1)</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>m2 = (m1+1):nmic</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">if </span><span>m1 == ref_mic</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> st1 = ref_st;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> ed1 = ref_ed;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">else</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> p = mic2refpair(m1);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> st1 = max(1,ref_st + besttdoa(p,t));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> ed1 = min(nsample, ref_ed + besttdoa(p,t));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">if </span><span>m2 == ref_mic</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> st2 = ref_st;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> ed2 = ref_ed;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">else</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> p = mic2refpair(m2);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> st2 = max(1,ref_st + besttdoa(p,t));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> ed2 = min(nsample, ref_ed + besttdoa(p,t));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> buf1 = x(m1,st1:ed1);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> buf2 = x(m2,st2:ed2);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> ener1 = sum(buf1(:).^2);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> ener2 = sum(buf2(:).^2);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> min_ed = min(ed1-st1, ed2-st2) + 1;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> tmp_localxcorr(m1,m2,t)</span><span style="color: rgb(0, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> = sum(</span><span style="color: rgb(0, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> buf1(1:min_ed) .* buf2(1:min_ed)</span><span style="color: rgb(0, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> / (ener1 * ener2));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">if </span><span>tmp_localxcorr(m1,m2,t) < 0</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> tmp_localxcorr(m1,m2,t) = 0;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> tmp_localxcorr(m2,m1,t) = tmp_localxcorr(m1,m2,t);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper outputs"><div class = 'S2'><span style="white-space: pre;"><span>disp(tmp_localxcorr(1,2:3,1));</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="64FF1C57" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="18" data-hashorizontaloverflow="false" data-testid="output_36" style="max-height: 261px; width: 1218px;"><div class="textElement"> 0.000698030059640 0.057655645698108</div></div></div></div><div class="inlineWrapper"><div class = 'S6'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>localxcorr = squeeze(sum(tmp_localxcorr,1));</span></span></div></div><div class="inlineWrapper outputs"><div class = 'S2'><span style="white-space: pre;"><span>disp(localxcorr);</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="E1231A85" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="437" data-hashorizontaloverflow="false" data-testid="output_37" style="max-height: 448px; width: 1218px;"><div class="textElement"> 1 ~ 3번 열
0.397481828090729 0.272221311968858 0.549831429382502
0.002428795872431 0.005991739961208 0.011247254873043
0.186393330242774 0 0.002722408652724
0.715193264562037 0.414665250919336 0.893735914333955
0.607615570764660 0.327711969658177 0.756122440655160
4 ~ 6번 열
0.645235383824703 0.470792710453255 0.690668782684766
0.004307078074065 0.002945669327416 0.004302080955592
0.141449692662963 0 0.006917319779727
1.235582243838077 0.708176751949193 1.011422635724574
1.140483857709310 0.572504011494178 0.925135324078567
7 ~ 9번 열
0.813100606742873 0.526430115188119 0.253807332047596
0.007271616997500 0.001950581914052 0.006902963426551
0.232380957256995 0.108274854763060 0.056979231306526
1.305816345787604 0.954577045883116 0.439465023250163
1.189136571512724 0.867979358789862 0.392843880577642
10 ~ 12번 열
0.218957123065576 0.281222542617182 0.549763359277866
0.005953145835015 0.000256417818234 0
0.158498588869339 0.199149532315087 0.128770584123641
0.388978914338482 0.494764907955565 1.233365173918296
0.360862824108992 0.347266154733443 1.017855431114984</div></div></div></div></div><h2 class = 'S4'><span>compute sum weight</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S0'><span style="white-space: pre;"><span>out_weight = ones(nmic, nframe) * ( 1 / nmic);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>alpha = 0.05;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>t = 1:nframe</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">if </span><span>sum(localxcorr(:,t)) == 0</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> localxcorr(:,t) = 1 / nmic;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> localxcorr(:,t) = localxcorr(:,t) / sum(localxcorr(:,t));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> localxcorr_sum_nonref = 0;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> localxcorr_sum = 0;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>m = 1:nmic</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">if </span><span>m == ref_mic</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> out_weight(m,t) = </span><span style="color: rgb(0, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> (1-alpha) * out_weight(m,max(1,t-1)) </span><span style="color: rgb(0, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> + alpha * localxcorr(m,t); </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">else</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> p = mic2refpair(m);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">if </span><span>noise_filter(p,t) == 0</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> out_weight(m,t) = </span><span style="color: rgb(0, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> (1-alpha) * out_weight(m,max(1,t-1)) </span><span style="color: rgb(0, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> + alpha * localxcorr(m,t); </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> localxcorr_sum_nonref = localxcorr_sum_nonref + localxcorr(m,t);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">if </span><span>sum(localxcorr(:,t)) == 0</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> out_weight(:,t) = 1;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>m = 1:nmic</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">if </span><span>m ~= ref_mic</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">if </span><span>(localxcorr(m,t) / localxcorr_sum_nonref) < (1 / (10 * (nmic-1)))</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> out_weight(m,t) = 0;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> out_weight(:,t) = out_weight(:,t) / sum(out_weight(:,t));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper outputs"><div class = 'S2'><span style="white-space: pre;"><span>disp(mic2refpair');</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="3A77B575" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="18" data-hashorizontaloverflow="false" data-testid="output_38" style="max-height: 261px; width: 1218px;"><div class="textElement"> 1 2 3 4 5</div></div></div></div><div class="inlineWrapper outputs"><div class = 'S5'><span style="white-space: pre;"><span>disp(out_weight);</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="30003BBE" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="437" data-hashorizontaloverflow="false" data-testid="output_39" style="max-height: 448px; width: 1218px;"><div class="textElement"> 1 ~ 3번 열
0.245885478956445 0.322110404440794 0.318524450164241
0 0 0
0.245382299188534 0 0
0.256094509453133 0.343865208632611 0.346968271223715
0.252637712401888 0.334024386926595 0.334507278612044
4 ~ 6번 열
0.261154873772821 0.310853617665045 0.308465072554650
0 0 0
0.166986883237902 0 0
0.291497846326962 0.353159632502212 0.354744087460624
0.280360396662314 0.335986749832743 0.336790839984726
7 ~ 9번 열
0.254467355389486 0.242976318106568 0.238440845782640
0 0 0
0.167137097825062 0.192496730621572 0.197170482242763
0.297011443074925 0.290255291181525 0.290678314921631
0.281384103710528 0.274271660090335 0.273710357052966
10 ~ 12번 열
0.234903319204833 0.232976367070724 0.228773877342686
0 0 0
0.198919412346919 0.199304727373134 0.198321628913216
0.291721769681798 0.294810648173794 0.298592118670037
0.274455498766450 0.272908257382348 0.274312375074061</div></div></div></div></div><div class = 'S10'><span></span></div><div class = 'S11'><span></span></div><h2 class = 'S4'><span>Channel sum</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S0'><span style="white-space: pre;"><span>out_x = zeros(1,nsample);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(34, 139, 34);">% figure;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">for </span><span>t = 1:nframe</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> ref_st = (t-1) * 4000 + 1;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> ref_ed = min(ref_st + 8000 - 1, nsample);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>m = 1:nmic </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">if </span><span>m == ref_mic</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> st = ref_st;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> ed = ref_ed;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">else</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> p = mic2refpair(m);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> st = max(1,ref_st + besttdoa(p,t));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> ed = min(nsample, ref_ed + besttdoa(p,t));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> triwin = triang(8000)';</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(34, 139, 34);">% if t == 1</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(34, 139, 34);">% triwin(1:4000) = 1;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(34, 139, 34);">% end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> diff = 0;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">if </span><span>(ref_ed - ref_st) ~= (ed - st) </span><span style="color: rgb(34, 139, 34);">% if buf is small (always)</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> diff = ref_ed - ed;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> out_x(ref_st+diff:ref_ed)</span><span style="color: rgb(0, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> = out_x(ref_st+diff:ref_ed)</span><span style="color: rgb(0, 0, 255);">...</span><span style="color: rgb(34, 139, 34);"> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> + (squeeze(x(m,st:ed))</span><span style="color: rgb(0, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> * out_weight(m,t)</span><span style="color: rgb(0, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> .* triwin(1:min(8000,ed-st+1))</span><span style="color: rgb(0, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> * overall_weight);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(34, 139, 34);">%.* triwin(min(8000,min_ed-ref_st+1))...</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(34, 139, 34);">%* out_weight(m,t));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(34, 139, 34);">% plot(out_x);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">while </span><span>ref_ed < nsample </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> ref_st = ref_st + 4000;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> ref_ed = min(ref_st + 8000 - 1, nsample);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>m = 1:nmic</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">if </span><span>m == ref_mic</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> st = ref_st;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> ed = ref_ed;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">else</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> p = mic2refpair(m);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> st = max(1,ref_st + besttdoa(p,t));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> ed = min(nsample, ref_ed + besttdoa(p,t));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">if </span><span>st > ed</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">continue</span><span>;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> buf = squeeze(x(m,st:ed));</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> diff = (ref_ed - ref_st) - (ed-st);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">if </span><span>diff > 0</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span class="warning_squiggle_rte">buf</span><span> = [buf, zeros(1,diff)];</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">else</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> buf = buf(1:end-diff);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> triwin = triang(8000)';</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> out_x(ref_st:ref_ed)</span><span style="color: rgb(0, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> = out_x(ref_st:ref_ed)</span><span style="color: rgb(0, 0, 255);">...</span><span style="color: rgb(34, 139, 34);"> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> + (buf</span><span style="color: rgb(0, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> * out_weight(m,t)</span><span style="color: rgb(0, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> .* triwin(1:min(8000,size(buf,2)))</span><span style="color: rgb(0, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> * overall_weight);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>audiowrite(outname,out_x,16000);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>out_x = audioread(outname);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>orig_out_x = audioread(orig_outname);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>subplot(4,1,1); plot(orig_out_x); title(</span><span style="color: rgb(160, 32, 240);">'original out x'</span><span>);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>subplot(4,1,2); plot(orig_out_x); title(</span><span style="color: rgb(160, 32, 240);">'my out x'</span><span>);</span></span></div></div><div class="inlineWrapper outputs"><div class = 'S2'><span style="white-space: pre;"><span>subplot(4,1,3); plot(out_x-orig_out_x); title(</span><span style="color: rgb(160, 32, 240);">'original - my'</span><span>);</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsFigure" uid="9A6DA2CA" data-scroll-top="null" data-scroll-left="null" data-testid="output_40" style="width: 1218px;"><div class="figureElement"><div class="figureContainingNode" style="width: 560px; max-width: 100%; display: inline-block;"><div class="GraphicsView" data-dojo-attach-point="graphicsViewNode,backgroundColorNode" id="uniqName_197_96" widgetid="uniqName_197_96" style="width: 100%; height: auto;"><img class="ImageView figureImage" data-dojo-attach-point="imageViewNode" draggable="false" ondragstart="return false;" id="uniqName_197_98" widgetid="uniqName_197_98" 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" style="width: 100%; height: auto;"></div></div></div></div></div></div><div class="inlineWrapper"><div class = 'S6'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>nshift = nwin / 2; </span><span style="color: rgb(34, 139, 34);">% 4000</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span>ed = (nframe-1)*nshift;</span></span></div></div><div class="inlineWrapper outputs"><div class = 'S2'><span style="white-space: pre;"><span>disp(mean((out_x-orig_out_x).^2));</span></span></div><div class = 'S3'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="669583B5" data-scroll-top="null" data-scroll-left="null" data-width="1188" data-height="18" data-hashorizontaloverflow="false" data-testid="output_41" style="max-height: 261px; width: 1218px;"><div class="textElement"> 2.165924584430555e-06</div></div></div></div></div><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S0'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">function </span><span>[table, l] = fill_all_comb(ipair, npair, ibest, nbest, tmp_row, table, l)</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> tmp_row(ipair) = ibest;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(34, 139, 34);">%fprintf('ipair: %d ibest: %d l: %d\n', ipair, ibest, l);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">if </span><span>ipair == npair </span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>j = 1:npair</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> table(l, j) = tmp_row(j);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> l = l + 1;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">else</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>ibest = 1:nbest</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> [table, l] = fill_all_comb(ipair + 1, npair, ibest, nbest, tmp_row, table, l);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">function </span><span>[max_val, idx] = maxk(list, k, nmask)</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> candi_list = zeros(length(list(:)),1);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>i = 2:(length(list(:))-1)</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">if </span><span>list(i-1) < list(i) && list(i+1) < list(i)</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> candi_list(i) = list(i);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(34, 139, 34);">% list(i-1) = -9999;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span style="color: rgb(34, 139, 34);">% list(i+1) = -9999;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S1'></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> max_val = zeros(k, 1);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> idx = zeros(k,1);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">for </span><span>i = 1:k</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> [max_val(i), idx(i)] = max(candi_list);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> st = max(idx(i)-nmask+1, 1);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> ed = min(length(candi_list(:)), idx(i)+nmask-1);</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> candi_list(st:ed) = 0;</span></span></div></div><div class="inlineWrapper"><div class = 'S1'><span style="white-space: pre;"><span> </span><span style="color: rgb(0, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S8'><span style="white-space: pre;"><span style="color: rgb(0, 0, 255);">end</span></span></div></div></div></div><br>
<!--
##### SOURCE BEGIN #####
clear;
close all;
clc;
format long;
dirname = 'sample11/';
outname = [dirname, 'enhan.wav'];
orig_outname = [dirname, 'orig_enhan.wav'];
paths = csvimport([dirname, 'wav.list'],'columns', {'path'});
[x1, sr] = audioread(paths{1});
[x2, sr] = audioread(paths{2});
[x3, sr] = audioread(paths{3});
[x4, sr] = audioread(paths{4});
[x5, sr] = audioread(paths{5});
x = [x1, x2, x3, x4, x5];
x = x';
nsample = size(x,2);
nmic = 5;
%npair = nmic - 1;
npair = nmic;
figure;plot(x(1,:));
%% make hamming window
%%
nwin = 16000; % 1 sec
% hamm_val = 0.54 - 0.46*cos(6.283185307*i/(window-1));
win = zeros(1,nwin);
for i = 1:nwin
win(i) = 0.54 - 0.46 * cos(6.283185307*(i-1)/(nwin-1));
end
figure; plot(win);
disp(win(1:10));
%% testing calculating xcorr
%%
npiece = 200;
nfft = 16384*2;
nbest = 2;
nmask = 5;
scroll = floor(nsample / (npiece+2));
stft1 = fft([x(1,scroll+1:(scroll+nwin)) .* win, zeros(1,nfft-nwin)]);
stft2 = fft([x(2,scroll+1:(scroll+nwin)) .* win, zeros(1,nfft-nwin)]);
numerator = stft1 .* conj(stft2);
ccorr = real(ifft(numerator ./ (abs(numerator))));
ccorr = [ccorr(end-479:end), ccorr(1:480)];
[best_ccorr, best_idx] = maxk(ccorr, nbest, nmask);
disp(scroll);
plot(ccorr);
disp(best_ccorr);
%% calculate avg_ccorr
%%
avg_ccorr = zeros(nmic, nmic);
for i = 1:npiece
st = i * scroll + 1;
ed = st + 16000 - 1;
if st + 16384 >= nsample
break;
end
for m1 = 1:(nmic-1)
avg_ccorr(m1, m1) = 0;
for m2 = (m1+1):nmic
stft1 = fft([x(m1,st:ed) .* win, zeros(1,nfft-nwin)]);
stft2 = fft([x(m2,st:ed) .* win, zeros(1,nfft-nwin)]);
numerator = stft1 .* conj(stft2);
ccorr = real(ifft(numerator ./ (abs(numerator))));
ccorr = [ccorr(end-479:end), ccorr(1:480)];
maxk_val = sum(maxk(ccorr, nbest, nmask));
avg_ccorr(m1, m2) = avg_ccorr(m1, m2) + (maxk_val);
avg_ccorr(m2, m1) = avg_ccorr(m1, m2);
end
end
end
avg_ccorr = avg_ccorr / (nbest * npiece);
disp(avg_ccorr);
fprintf('%.8f\n', sum(avg_ccorr/nmic));
disp(sum(avg_ccorr,1));
disp(sum(avg_ccorr,1)/nmic);
[dummy, ref_mic] = max(sum(avg_ccorr));
disp(ref_mic);
%% calculating scaling factor
%%
nsegment = 10;
max_val = zeros(nmic, 1);
if size(x,2) <= 160000 % 10 seconds
for m = 1:nmic
max_val(m) = max(abs(x(m,:)));
end
else
if size(x,2) < 1600000 % 100 seconds
nsegment = floor(size(x,2) / 160000);
end
scroll = floor(size(x,2) / nsegment);
max_val_candidate = zeros(nmic, nsegment);
for s = 0:(nsegment-1)
st = s * scroll + 1;
ed = st + 160000 - 1;
for m = 1:nmic
max_val_candidate(m,s+1) = max(abs(x(m,st:ed)));
end
end
for m = 1:nmic
sorted = sort(max_val_candidate(m,:), 'ascend');
if length(sorted(:)) > 2
max_val(m) = sorted(end/2 + 1);
else
max_val(m) = sorted;
end
end
end
overall_weight = (0.3 * nmic) / sum(max_val);
disp(max_val');
disp(overall_weight);
%% compute total number of delays
%%
% int totalNumDelays
% % = (int)((m_frames - (*m_config).windowFrames - m_biggestSkew - m_UEMGap)/((*m_config).
% rate*m_sampleRateInMs));
% sr_in_ms = 16000 / 1000; % 16
% too complicated. I should do hard coding
nframe = floor(( nsample - 8000 ) / (4000));
disp(nframe);
%% recreating hamming window
%%
nwin = 8000; % 0.5 sec
nfft = 16384;
win = zeros(1,nwin);
for i = 1:nwin
win(i) = 0.54 - 0.46 * cos(6.283185307*(i-1)/(nwin-1));
end
figure; plot(win);
% Marginst for delays in frames: 480 in ms: 30
disp(16000 * 30 / 1000);
%% compute TDOA
nbest = 4;
micpair = zeros(nmic, npair);
gcc_nbest = zeros(npair, nframe, nbest);
tdoa_nbest = zeros(npair, nframe, nbest);
lrfilp_gcc = zeros(npair, nframe, nfft);
for m = 1:nmic
p = 1; % pair idx
for i = 1:nmic
micpair(m,p) = i;
p = p + 1;
end
end
for t = 1:(nframe)
st = (t-1) * 4000 + 1;
ed = st + nwin - 1;
for p = 1:npair
m = micpair(ref_mic,p);
stft_ref = fft([x(ref_mic,st:ed) .* win, zeros(1,nfft-nwin)]);
stft_m = fft([x(m,st:ed) .* win, zeros(1,nfft-nwin)]);
numerator = stft_m .* conj(stft_ref);
gcc = real(ifft(numerator ./ (eps+abs(numerator))));
gcc = [gcc(end-479:end), gcc(1:480)];
[gcck, tdoak] = maxk(gcc, nbest, nmask);
gcc_nbest(p,t,:) = gcck;
tdoa_nbest(p,t,:) = tdoak;
tdoa_nbest(p,t,:) = tdoa_nbest(p,t,:) - (481); % index shifting
end
end
disp(squeeze(gcc_nbest(:,:,1)));
disp(squeeze(gcc_nbest(:,:,2)));
disp(squeeze(tdoa_nbest(:,:,1)));
disp(squeeze(tdoa_nbest(:,:,2)));
%% find noise threshold
%%
th_idx = floor((0.1 * nframe)) + 1;
sorted = sort(sum(gcc_nbest(:,:,1),1) - sum(gcc_nbest(ref_mic,:,1),1), 'ascend');
threshold = sorted(th_idx)/(nmic-1);
% Computing the optimum delays
disp(threshold);
%% noise filtering
%%
noise_filter = zeros(npair, nframe);
for p = 1:npair
for t = 1:nframe
if gcc_nbest(p,t,1) < threshold
noise_filter(p,t) = 1;
if t == 1 % it's silence
gcc_nbest(p,t,:) = 0; % masking with discouraging values
gcc_nbest(p,t,1) = 1; % only one path
tdoa_nbest(p,t,:) = 480; % masking with discouraging values
tdoa_nbest(p,t,1) = 0;
else
tdoa_nbest(p,t,:) = tdoa_nbest(p,t-1,:);
end
end
if p == ref_mic
gcc_nbest(p,t,:) = 0; % masking with discouraging values
gcc_nbest(p,t,1) = 1; % only one path
tdoa_nbest(p,t,:) = 0;
end
end
end
disp(gcc_nbest(:,:,1));
disp(tdoa_nbest(:,:,1));
disp(gcc_nbest(:,:,2));
disp(tdoa_nbest(:,:,2));
%% single channel viterbi - emission, trans prob
%%
emission1 = zeros(npair, nframe, nbest);
diff1 = zeros(npair, nframe, nbest, nbest); % do not using 1st idx
transition1 = zeros(npair, nframe, nbest, nbest); % do not using 1st idx
for p = 1:npair
for t = 1:nframe
for n = 1:nbest
if gcc_nbest(p,t,n) <= 0
emission1(p,t,n) = -1000;
else
emission1(p,t,n) = log10(gcc_nbest(p,t,n) / sum(gcc_nbest(p,t,:)));
end
end
end
end
for p = 1:npair
for t = 2:nframe
for n = 1:nbest
for nprev = 1:nbest
diff1(p,t,n,nprev) = abs(tdoa_nbest(p,t,n) - tdoa_nbest(p,t-1,nprev));
end
end
end
end
for p = 1:npair
for t = 2:nframe
for n = 1:nbest
for nprev = 1:nbest
% there is a computational bug.
% disp((2+maxdiff1(p,t,n)));
% disp(diff1(p,t,n,nprev));
% disp(maxdiff1(p,t,n));
% disp(log10(481/482));
% disp(1 + maxdiff1(p,t,n) - diff1(p,t,n,nprev));
% disp((2+maxdiff1(p,t,n)));
maxdiff1 = max(diff1(p,t,:));
nume = (1 + maxdiff1 - diff1(p,t,n,nprev));
deno = (2 + maxdiff1);
transition1(p,t,n,nprev) = log(nume / deno) / log(10);
end
end
end
end
%% single channel viterbi - searching
%%
nbest2 = 2;
score1 = zeros(npair, nframe, nbest, nbest); % tmp variable
score1_table = zeros(npair, nframe, nbest);
back1_table = zeros(npair, nframe, nbest);
bestpath1 = zeros(npair, nframe, nbest2); % state idx stored.
% original beamformit
dC = ones(npair, nframe, nbest) * -1000;
tC = ones(npair, nframe, nbest);
viterbi_score = zeros(npair, 1);
R = ones(npair, nframe); % best prev state
F = ones(npair, nframe);
forwardTrans = zeros(npair, nframe, nbest);
selfLoopTrans = zeros(npair, nframe, nbest);
bestpath1(:,:,1) = 1;
bestpath1(:,:,2) = 2;
for p = 1:npair
for n = 1:nbest
score1(p,1,n,:) = emission1(p,1,n); % broadcasting
score1_table(p,1,n) = emission1(p,1,n);
end
end
for p = 1:npair
for n = 1:nbest
dC(p,1,n) = emission1(p,1,n);
end
end
for p = 1:npair
for t = 2:nframe
for n = 1:nbest
best_n_prev = R(p,t-1);
back1_table(p,t,1) = best_n_prev;
forwardTrans(p,t,n) = dC(p,t-1,best_n_prev) + 25 * transition1(p,t,n,best_n_prev);
selfLoopTrans(p,t,n) = dC(p,t-1,n) + 25 * transition1(p,t,n,n);
if selfLoopTrans(p,t,n) >= forwardTrans(p,t,n)
dC(p,t,n) = selfLoopTrans(p,t,n) + emission1(p,t,n);
tC(p,t,n) = tC(p,t-1,n);
else
dC(p,t,n) = forwardTrans(p,t,n) + emission1(p,t,n);
tC(p,t,n) = t;
end
end
[dummy, R(p,t)] = max(dC(p,t,:));
F(p,t) = tC(p,t,R(p,t));
end
st = F(p,end);
bestpath1(p,st:end,1) = R(p,end);
while st > 2
ed = st - 1;
st = F(p,ed);
bestpath1(p,st:ed,1) = R(p,ed);
end
viterbi_score(p) = dC(p,end,R(p,end));
end
disp(bestpath1(:,:,1));
disp(viterbi_score');
%% single channel viterbi - 2-best path
%%
gcc_nbest_copy = gcc_nbest;
emission1_copy = emission1;
bestpath1(:,:,2) = bestpath1(:,:,1);
for p = 1:npair
for t = 1:nframe
best1 = bestpath1(p,t,1);
gcc_nbest_copy(p,t,best1) = 0;
end
end
for p = 1:npair
for t = 1:nframe
for n = 1:nbest
if gcc_nbest_copy(p,t,n) > 0
emission1_copy(p,t,n) = log10(gcc_nbest_copy(p,t,n) / sum(gcc_nbest_copy(p,t,:)));
else
emission1_copy(p,t,n) = -1000;
end
end
end
end
dC = zeros(npair, nframe, nbest);
tC = ones(npair, nframe, nbest);
viterbi_score = zeros(npair, 1);
R = ones(npair, nframe); % best prev state
F = ones(npair, nframe);
forwardTrans = zeros(npair, nframe, nbest);
selfLoopTrans = zeros(npair, nframe, nbest);
for p = 1:npair
for n = 1:nbest
dC(p,1,n) = emission1_copy(p,1,n);
end
end
for p = 1:npair
for t = 2:nframe
for n = 1:nbest
best_n_prev = R(p,t-1);
back1_table(p,t,2) = best_n_prev;
forwardTrans(p,t,n) = dC(p,t-1,best_n_prev) + 25 * transition1(p,t,n,best_n_prev);
selfLoopTrans(p,t,n) = dC(p,t-1,n) + 25 * transition1(p,t,n,n);
if selfLoopTrans(p,t,n) >= forwardTrans(p,t,n)
dC(p,t,n) = selfLoopTrans(p,t,n) + emission1_copy(p,t,n);
tC(p,t,n) = tC(p,t-1,n);
else
dC(p,t,n) = forwardTrans(p,t,n) + emission1_copy(p,t,n);
tC(p,t,n) = t;
end
end
[dummy, R(p,t)] = max(dC(p,t,:));
F(p,t) = tC(p,t,R(p,t));
end
st = F(p,end);
bestpath1(p,st:end,2) = R(p,end);
while st > 2
ed = st - 1;
st = F(p,ed);
bestpath1(p,st:ed,2) = R(p,ed);
end
viterbi_score(p) = dC(p,end,R(p,end));
end
disp(squeeze(bestpath1(1,:,:))');
disp(viterbi_score');
%% plot path
%%
% https://kr.mathworks.com/help/matlab/ref/scatter.html
figure;
emission_plot = zeros(npair, 3, nbest * nframe);
for p = 1:npair
for ibest = 1:nbest
for iframe = 1:nframe
emission_plot(p,1,(ibest-1) * nframe + iframe) = iframe;
emission_plot(p,2,(ibest-1) * nframe + iframe) = ibest;
emission_plot(p,3,(ibest-1) * nframe + iframe) = 1 + 400 * 10^(emission1(p, iframe, ibest));
end
end
end
hold on;
for p=1:npair
subplot(npair,1,p);
scatter(...
squeeze(emission_plot(p, 1,:)), ... % x range (x data for scatter)
squeeze(emission_plot(p, 2,:)), ... % y range (y data for scatter)
squeeze(emission_plot(p, 3,:)), ... % radius
'filled');
axis ij;
ylim([0 nbest+1]); % for readability
end
hold on;
max_linewidth = 1+max(max(max(max(transition1))));
for p=1:npair
subplot(npair, 1, p);
for t = 2:nframe
for n = 1:nbest
for nprev = 1:nbest
line([t-1 t], ... % x data
[nprev n], ... % y data
'LineWidth', 10^(transition1(p,t,n,nprev))*3,... % tuning
'Color','cyan');
end
end
end
end
hold on;
for p = 1:npair
subplot(npair,1,p);
for t = 2:nframe
line([t-1 t], [bestpath1(p,t-1,1) bestpath1(p,t,1)], ...
'LineWidth', 2, ...
'Color', 'red');
line([t-1 t], [bestpath1(p,t-1,2) bestpath1(p,t,2)], ...
'LineWidth', 2, ...
'Color', 'black');
end
end
%% multi channel viterbi - state define
%%
nbest2 = 2;
nstate = nbest2 ^ npair;
g = zeros(nstate, npair);
tmp_row = zeros(3,1);
l = 1;
for ibest = 1:nbest2
[g, l] = fill_all_comb(1, npair, ibest, nbest2, tmp_row, g, l);
end
disp(g);
%% multi channel viterbi - emission, trans prob
emission2 = zeros(nframe, nstate);
diff2 = zeros(nmic, nframe, nbest, nbest); % do not using 1st idx
transition2 = zeros(nframe, nstate, nstate); % do not using 1st idx
for t = 1:nframe
for l = 1:nstate
for m = 1:npair
ibest = bestpath1(m, t, g(l,m));
if gcc_nbest(m,t,ibest) > 0
emission2(t, l)...
= emission2(t, l)...
+ log10(gcc_nbest(m,t,ibest));
end
end
end
end
for t = 2:nframe
maxdiff2 = 0;
for ibest = 1:nbest
for jbest = 1:nbest
for m = 1:npair-1 % why -1?
diff2(m,t,ibest, jbest) = abs(tdoa_nbest(m,t,ibest) - tdoa_nbest(m,t-1,jbest));
if maxdiff2 < diff2(m,t,ibest, jbest)
maxdiff2 = diff2(m,t,ibest, jbest);
end
end
end
end
diff2(:,t,:,:) = log10( (1 + maxdiff2 - diff2(:,t,:,:)) / (2 + maxdiff2) );
for l = 1:nstate
for lprev = 1:nstate
for m = 1:npair-1 % why -1?
ibest = bestpath1(m, t, g(l,m));
jbest = bestpath1(m, t-1, g(lprev,m));
transition2(t,l,lprev)...
= transition2(t,l,lprev)...
+ diff2(m,t,ibest,jbest);
end
end
end
end
disp(emission2(2,end));
disp(transition2(2,:,1));
%% multi channel viterbi - searching
back2_table = zeros(nframe, nstate);
bestpath2 = ones(nframe, 1); % state idx stored.
dC = ones(nframe, nstate) * -1000;
tC = ones(nframe, nstate);
R = ones(nframe, 1); % best prev state
F = ones(nframe, 1);
forwardTrans = zeros(nframe, nstate);
selfLoopTrans = zeros(nframe, nstate);