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Programming language: Python
License: Apache License 2.0
Tags: Audio    

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README

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

This doc contains general info. Click here for the complete wiki. For a more generic intro to audio data handling read this article

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

Installation

  • Clone the source of this library: git clone https://github.com/tyiannak/pyAudioAnalysis.git
  • Install dependencies: pip install -r ./requirements.txt
  • Install using pip: pip install -e .

An audio classification example

More examples and detailed tutorials can be found at the 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.extract_features_and_train(["classifierData/music","classifierData/speech"], 1.0, 1.0, aT.shortTermWindow, aT.shortTermStep, "svm", "svmSMtemp", False)
aT.file_classification("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 this README file, to bettern understand how to use this library one should read the following:

For Matlab-related audio analysis material check this book.

Author

Theodoros Giannakopoulos, Director of Machine Learning at Behavioral Signals