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https://github.com/tyiannak/pyAudioAnalysis

Python Audio Analysis Library: Feature Extraction, Classification, Segmentation and Applications
https://github.com/tyiannak/pyAudioAnalysis

audio audio-analysis-tasks audio-data machine-learning pyaudioanalysis python signal-processing

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Python Audio Analysis Library: Feature Extraction, Classification, Segmentation and Applications

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README 

        

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



*This is general info. Click [here](https://github.com/tyiannak/pyAudioAnalysis/wiki) for the complete wiki and [here](https://hackernoon.com/audio-handling-basics-how-to-process-audio-files-using-python-cli-jo283u3y) for a more generic intro to audio data handling*



## News

 * [2022-01-01] If you are not interested in training audio models from your own data, you can check the [Deep Audio API](https://labs-repos.iit.demokritos.gr/MagCIL/deep_audio_api.html), 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](https://github.com/tyiannak/deep_audio_features) deep audio classification and feature extraction using CNNs and Pytorch 

 * Check out [paura](https://github.com/tyiannak/paura) a Python script for realtime recording and analysis of audio data



## 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](https://github.com/tyiannak/pyAudioAnalysis/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



```python

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:

  * [Audio Handling Basics: Process Audio Files In Command-Line or Python](https://hackernoon.com/audio-handling-basics-how-to-process-audio-files-using-python-cli-jo283u3y), if you want to learn how to handle audio files from command line, and some basic programming on audio signal processing. Start with that if you don't know anything about audio. 

  * [Intro to Audio Analysis: Recognizing Sounds Using Machine Learning](https://hackernoon.com/intro-to-audio-analysis-recognizing-sounds-using-machine-learning-qy2r3ufl) This goes a bit deeper than the previous article, by providing a complete intro to theory and practice of audio feature extraction, classification and segmentation (includes many Python examples).

 * [The library's wiki](https://github.com/tyiannak/pyAudioAnalysis/wiki)

 * [How to Use Machine Learning to Color Your Lighting Based on Music Mood](https://hackernoon.com/how-to-use-machine-learning-to-color-your-lighting-based-on-music-mood-bi163u8l). An interesting use-case of using this lib to train a real-time music mood estimator.

  * A more general and theoretic description of the adopted methods (along with several experiments on particular use-cases) is presented [in this publication](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0144610). *Please use the following citation when citing pyAudioAnalysis in your research work*:

```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](http://www.amazon.com/Introduction-Audio-Analysis-MATLAB%C2%AE-Approach/dp/0080993885).



## Author




[Theodoros Giannakopoulos](https://tyiannak.github.io),

Principal Researcher of Multimodal Machine Learning at the [Multimedia Analysis Group of the Computational Intelligence Lab (MagCIL)](https://labs-repos.iit.demokritos.gr/MagCIL/index.html) of the Institute of Informatics and Telecommunications, of the National Center for Scientific Research "Demokritos"