This doc contains general info. Click here for the complete wiki

• [2020-03-20] pip package has been updated version 0.3.0
• pyAudioAnalysis master [2019-11-19] contains major refactoring changes mainly in feature extraction. Please report possible issues that have not been fixed, or inconsistencies in the documentation.
• Check the tutorial for the course "Multimodal Information Processing & Analysis", MSc in Data Science in NCSR-D
• Check out paura a python script for realtime recording and analysis of audio data
• Check pyVisualizeMp3Tags a Python script to visualize mp3 tags and lyrics
• pyAudioAnalysis [2018-08-12] now ported to Python 3

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

• 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 .

(also works with pip3 now)

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

Apart from the current README and the wiki, a more general and theoretic description of the adopted methods (along with several experiments on particular use-cases) is presented in this publication. 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}
}

For Matlab-related audio analysis material check this book.

Theodoros Giannakopoulos, Director of Machine Learning at Behavioral Signals