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

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