This doc contains general info. Click here for the complete wiki
- 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" of the MSc in Data Science in NCSR Demokritos
- Check pyVisualizeMp3Tags a Python script to visualize mp3 tags and lyrics
- Check out paura a python script for realtime recording and analysis of audio data
- pyAudioAnalysis [2018-08-12] now compatible with 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