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test.py
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from scipy.fftpack import fft,ifft
from scipy.io import wavfile
import numpy as np
from matplotlib.pyplot import *
from scipy.signal import spectrogram
from peakdetect import detect_peaks
from scipy.signal import get_window
notes = dict({
55:"(LOW)",
65.41:"C2",
69.3:"C#2",
73.42:"D2",
77.78:"D#2",
82.41:"E2",
87.31:"F2",
92.5:"F#2",
98:"G2",
103.83:"G#2",
110:"A2",
116.54:"A#2",
123.47:"B2",
130.81:"C3",
138.59:"C#3",
146.83:"D3",
155.56:"D#3",
164.81:"E3",
174.61:"F3",
185:"F#3",
196:"G3",
207.65:"G#3",
220:"A3",
233.08:"A#3",
246.94:"B3",
261.63:"C4",
277.18:"C#4",
293.66:"D4",
311.13:"D#4",
329.63:"E4",
349.23:"F4",
369.99:"F#4",
392:"G4",
415.3:"G#4",
440:"A4",
466.16:"A#4",
493.88:"B4",
523.25:"C5",
554.37:"C#5",
587.33:"D5",
622.25:"D#5",
659.26:"E5",
698.46:"F5",
739.99:"F#5",
783.99:"G5",
830.61:"G#5",
880:"A5",
932.33:"A#5",
987.77:"B5",
1046.5:"C6",
1108.7:"C#6",
1174.7:"D6",
1244.5:"D#6",
1318.5:"E6",
1400:"(HIGH)"
})
def find_closest_note(freq):
dist = 99999
result = ""
for i in notes.keys():
newDist = abs(freq-i)
if newDist<dist:
dist = newDist
result = notes[i]
key=i
return result,key
samplerate, data = wavfile.read('synth.wav')
data=np.average(data,axis=1)
'''
f=fft(data[2048:4096])
plot(20*np.log10(abs(f)))
show()
'''
def normalize_volume(stream,window_size):
l=len(stream)
peak=max(stream)
new_stream=np.zeros(l)
window=get_window('blackman',window_size)
avg=np.average(abs(data))/4
for i in range((window_size-1)/2,l-((window_size-1)/2)):
local_avg=np.average(abs(stream[i-((window_size-1)/2):i+((window_size-1)/2)+1]),weights=window)
#if local_avg==0 : break
new_stream[i]=stream[i]*avg/local_avg
if i%10000==0: print i
return new_stream
n=normalize_volume(data,129)
wavfile.write('scaled.wav',samplerate,n)
f, t, Sxx = spectrogram(data, samplerate,nperseg=2048,noverlap=2048/4,nfft=2048,window='blackman')
subplot(321)
pcolormesh(t, f[:], np.log10(Sxx[:,:]))
cep=20*np.log10(abs(ifft(20*np.log10(Sxx),axis=0)))[:Sxx.shape[0]/2,:]
subplot(322)
pcolormesh(cep)
subplot(323)
plot(abs(cep[:,200]))
#plot(20*np.log10(Sxx[:,200]))
#plot(abs(cep[:,90]))
#plot(abs(cep[:,60]))
subplot(324)
plot(abs(cep[:,300]))
#plot(20*np.log10(Sxx[:,300]))
subplot(325)
plot(abs(cep[:,400]))
#plot(20*np.log10(Sxx[:,400]))
show()
frequencies=[]
notes_played=[]
'''
for i in range(Sxx.shape[1]):
peaks=detect_peaks(Sxx[:,i], mph=0.002, mpd=20, show=False)
if peaks.size>0:
fundamental=f[peaks[0]]
note,fundamental=find_closest_note(fundamental)
frequencies.append(fundamental)
notes_played.append(note)
else:
frequencies.append(0)
notes_played.append('(LOW)')
plot(frequencies)
show()
'''