Python Audio Analysis Library: Feature Extraction, Classification, Segmentation and Applications
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A Python library for audio feature extraction, classification, segmentation and applications

This is general info. Click here for the complete wiki and here for a more generic intro to audio data handling


  • [2022-01-01] If you are not interested in training audio models from your own data, you can check the Deep Audio API, were you can directly send audio data and receive predictions with regards to the respective audio content (speech vs silence, musical genre, speaker gender, etc).
  • [2021-08-06] deep-audio-features deep audio classification and feature extraction using CNNs and Pytorch
  • Check out paura a Python script for realtime recording and analysis of audio data


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


  • Clone the source of this library: git clone
  • 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 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:

  title={pyAudioAnalysis: An Open-Source Python Library for Audio Signal Analysis},
  author={Giannakopoulos, Theodoros},
  journal={PloS one},
  publisher={Public Library of Science}

For Matlab-related audio analysis material check this book.


Theodoros Giannakopoulos, Principal Researcher of Multimodal Machine Learning at the Multimedia Analysis Group of the Computational Intelligence Lab (MagCIL) of the Institute of Informatics and Telecommunications, of the National Center for Scientific Research "Demokritos"

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