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C++ library for audio and music analysis, description and synthesis, including Python bindings
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src Merge pull request #943 from pabloEntropia/musicnn_and_vggish_algorithms Jan 23, 2020
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.gitmodules Fix essentia-models submodule path Jan 21, 2020
.travis.yml Fix setting env variables in essentia-builds Dec 12, 2019
ACKNOWLEDGEMENTS First commit :-) Jun 3, 2013
AUTHORS Update AUTHORS Dec 22, 2016
COPYING.txt First commit :-) Jun 3, 2013
Changelog Essentia 2.0 release Oct 28, 2013
Essentia Licensing.txt Update copyright year in Essentia Licencing.txt Jan 22, 2020 Update instructions for building static dependencies in FAQ Jan 10, 2020 Add script for python packaging May 9, 2018 Fix broken links in Jun 5, 2018
VERSION Change version to 2.1-beta6-dev Aug 3, 2019 Updated script Sep 17, 2015 Several improvements related to Waf Dec 3, 2019
essentia.vcproj First commit :-) Jun 3, 2013
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essentiaVC80.vcproj First commit :-) Jun 3, 2013 Remove install dependency on tensorflow for essentia-tensorflow wheels Jan 14, 2020
waf Update waf to 2.0.10 Aug 29, 2018
wscript Add Tensorflow related algorithms Dec 9, 2019


Build Status

Essentia is an open-source C++ library for audio analysis and audio-based music information retrieval released under the Affero GPL license. It contains an extensive collection of reusable algorithms which implement audio input/output functionality, standard digital signal processing blocks, statistical characterization of data, and a large set of spectral, temporal, tonal and high-level music descriptors. The library is also wrapped in Python and includes a number of predefined executable extractors for the available music descriptors, which facilitates its use for fast prototyping and allows setting up research experiments very rapidly. Furthermore, it includes a Vamp plugin to be used with Sonic Visualiser for visualization purposes. Essentia is designed with a focus on the robustness of the provided music descriptors and is optimized in terms of the computational cost of the algorithms. The provided functionality, specifically the music descriptors included in-the-box and signal processing algorithms, is easily expandable and allows for both research experiments and development of large-scale industrial applications.

Documentation online:


The library is cross-platform and currently supports Linux, Mac OS X, Windows, iOS and Android systems. Read installation instructions:

You can download and use prebuilt static binaries for a number of Essentia's command-line music extractors instead of installing the complete library

Quick start

Quick start using python:

Command-line tools to compute common music descriptors:

Asking for help


Official releases:

Github branches:

  • master: the most updated version of Essentia (Ubuntu 14.10 or higher, OSX); if you got any problem - try it first.

If you use example extractors (located in src/examples), or your own code employing Essentia algorithms to compute descriptors, you should be aware of possible incompatibilities when using different versions of Essentia.

How to contribute

We are more than happy to collaborate and receive your contributions to Essentia. The best practice of submitting your code is by creating pull requests to our GitHub repository following our contribution policy. By submitting your code you authorize that it complies with the Developer's Certificate of Origin. For more details see:

You are also more than welcome to suggest any improvements, including proposals for new algorithms, etc.

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