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This is general info. Click here for the complete wiki and here for a more generic intro to audio data handling
pyAudioAnalysis is a Python library covering a wide range of audio analysis tasks. Through pyAudioAnalysis you can:
git clone https://github.com/tyiannak/pyAudioAnalysis.gitpip install -r ./requirements.txtpip install -e .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 this README file, to bettern understand how to use this library one should read the following:
python
@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, 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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