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Scientific Audio

Curated list of python software and packages related to scientific research in audio


faroitfaroit/awesome-python-scientific-audioUpdated 501 50 75


Python for Scientific Audio Awesome

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The aim of this repository is to create a comprehensive, curated list of python software/tools related and used for scientific research in audio/music applications.


Audio Related Packages

  • Total number of packages: 72
  • Python version compatibility: 69 63 (²indicates a python 2 only package, ³indicates a python 3 only package; the remaining package support python 2+3).


Transformations - General DSP

Feature extraction

Speech Processing

Perceptial Models - Auditory Models

Source Separation

Music Information Retrieval

  • Catchy :octocat: - Corpus Analysis Tools for Computational Hook Discovery.
  • dejavu² :octocat: 📦 - Audio fingerprinting and recognition.
  • Madmom :octocat: 📦 - MIR packages with strong focus on beat detection, onset detection and chord recognition.
  • mir_eval :octocat: 📦 - Common scores for various MIR tasks. Also includes bss_eval implementation.
  • msaf :octocat: 📦 - Music Structure Analysis Framework.
  • librosa :octocat: 📦 - General audio and music analysis.
  • rp_extract² :octocat: - Rhythm Pattern music feature extractor.

Deep Learning

Symbolic Music - MIDI - Musicology

Realtime applications

Web Audio

  • TimeSide² :octocat: - high level audio analysis, imaging, transcoding, streaming and labelling.

Audio related APIs and Datasets

Wrappers for Audio Plugins



Scientific Papers

Other Resources

Related lists

There is already PythonInMusic but it is not up to date and includes too many packages of special interest that are mostly not relevant for scientific applications. Awesome-Python is large curated list of python packages. However, the audio section is very small.


Your contributions are always welcome! Please take a look at the contribution guidelines first.

I will keep some pull requests open if I'm not sure whether those libraries are awesome, you could vote for them by adding 👍 to them.


License: CC BY 4.0

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