A Python library for audio feature extraction, classification, segmentation and applications

This doc contains general info. Click [here] (https://github.com/tyiannak/pyAudioAnalysis/wiki) for the complete wiki

News

• Check out paura a python script for realtime recording and analysis of audio data
• January 2017: mp3 files are also supported for single file feature extraction, classification and segmentation (using pydub library)
• September 2016: New segment classifiers (from sklearn): random forests, extra trees and gradient boosting
• August 2016: Update: mlpy no longer used. SVMs, PCA, etc performed through scikit-learn
• August 2016: Update: Dependencies have been simplified
• January 2016: [PLOS-One Paper regarding pyAudioAnalysis] (http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0144610) (please cite!)

General

pyAudioAnalysis is a Python library covering a wide range of audio analysis tasks. Through pyAudioAnalysis you can:

• Extract audio features and representations (e.g. mfccs, spectrogram, chromagram)
• Classify unknown sounds
• Train, parameter tune and evaluate classifiers of audio segments
• Detect audio events and exclude silence periods from long recordings
• Perform supervised segmentation (joint segmentation - classification)
• Perform unsupervised segmentation (e.g. speaker diarization)
• Extract audio thumbnails
• Train and use audio regression models (example application: emotion recognition)
• Apply dimensionality reduction to visualize audio data and content similarities

Installation

• Install dependencies:

pip install numpy matplotlib scipy sklearn hmmlearn simplejson eyed3 pydub

• Clone the source of this library:

git clone https://github.com/tyiannak/pyAudioAnalysis.git

An audio classification example

More examples and detailed tutorials can be found [at the wiki] (https://github.com/tyiannak/pyAudioAnalysis/wiki)

pyAudioAnalysis provides easy-to-call wrappers to execute audio analysis tasks. Eg, this code first trains an audio segment classifier, given a set of WAV files stored in folders (each folder representing a different class) and then the trained classifier is used to classify an unknown audio WAV file

from pyAudioAnalysis import audioTrainTest as aT
aT.featureAndTrain(["classifierData/music","classifierData/speech"], 1.0, 1.0, aT.shortTermWindow, aT.shortTermStep, "svm", "svmSMtemp", False)
aT.fileClassification("data/doremi.wav", "svmSMtemp","svm")
Result:
(0.0, array([ 0.90156761,  0.09843239]), ['music', 'speech'])

In addition, command-line support is provided for all functionalities. E.g. the following command extracts the spectrogram of an audio signal stored in a WAV file: python audioAnalysis.py fileSpectrogram -i data/doremi.wav

Further reading

Apart from the current README file and [the wiki] (https://github.com/tyiannak/pyAudioAnalysis/wiki), a more general and theoretic description of the adopted methods (along with several experiments on particular use-cases) is presented [in this publication] (http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0144610). Please use the following citation when citing pyAudioAnalysis in your research work:

@article{giannakopoulos2015pyaudioanalysis,
  title={pyAudioAnalysis: An Open-Source Python Library for Audio Signal Analysis},
  author={Giannakopoulos, Theodoros},
  journal={PloS one},
  volume={10},
  number={12},
  year={2015},
  publisher={Public Library of Science}
}

Finally, for Matlab-related audio analysis material check this book.

Author

[Theodoros Giannakopoulos] (http://www.di.uoa.gr/~tyiannak), Postdoc researcher at NCSR Demokritos, Athens, Greece