musif

Python library for Music Feature Extraction and Analysis, developed by the Didone Project.

Documentation

To read the documentation, please see the website at: https://musif.didone.eu Includes definitions for musif's functions and classes, definitions for all types of features that musif extracts, as well as example code for using musif.

You will find also two tutorials:

• A basic Tutorial, to just start using musif and extracting some features and even running some ML experiments with them.
• An Advanced Tutorial, to extract features of different corpora and create your own hooks and features.

Installation

To install the latest version of musif, just run: pip install musif which will download musif and all its necessary dependencies.

(Good practice is to update your package manager: python3 -m pip install –-upgrade pip)

music21 and jSymbolic features

Currently, musif is able to process and integrate basic music21 features.

For jSymbolic features, musif currently does not support the integration of these features, but a tutorial will be provided to manually merge them into musif's dataframe.

jSymbolic installation

Java JRE >= 8 must be installed in your OS. Download jSymbolic from https://sourceforge.net/projects/jmir/files/jSymbolic/

Important: right now music21 features are NOT guaranteed to be compatible with musif's cache system. Native musif's features work with cache system just fine.

Example

Check and run run_extraction_example.py to see a initial script for extracting xml files by using musif.

Testing and features extraction

Apart from the documentation of musif, where Tutorials and example code can be found, please feel free to clone and check this repository, where musif is used to extract features from different corpuses https://github.com/DIDONEproject/music_symbolic_features

References

1. A. Llorens, F. Simonetta, M. Serrano, and Á. Torrente, “musif: a Python package for symbolic music feature extraction,” in Proceedings of the Sound and Music Computing Conference, Stockholm, Sweden, 2023.
2. F. Simonetta, A. Llorens, M. Serrano, E. García-Portugués, and Á. Torrente, “Optimizing Feature Extraction for Symbolic Music,” in Proceedings of the 24th International Society for Music Information Retrieval Conference, Milan, Nov. 2023.

Changelog

See CHANGELOG.md for the full release history.