A Python3 package (and command-line tool) for computing and visualizing the notation differences between two music scores.
musicdiff is focused on visible notation differences, not only on audible musical differences. For example, two tied eighth notes are considered different from a single quarter note. And two beamed 16th notes are considered different from two unbeamed 16th notes. This makes musicdiff particularly useful for assessing the results of Optical Music Recognition software.
musicdiff is derived from: music-score-diff by Francesco Foscarin.
Depends on music21 (version 9.1+), numpy, and converter21 (version 3.0+). You also will need to configure music21 (instructions here) to display a musical score (e.g. with MuseScore). Requires Python 3.10+.
On the command line:
python3 -m musicdiff file1.musicxml file2.krn
positional arguments:
file1 first music score file to compare (any format music21 can parse)
file2 second music score file to compare (any format music21 can parse)
The musicdiff command line tool will display two rendered score PDFs that have the differences highlighted with color and descriptive text.
musicdiff is also a package, with APIs you can call in your own code. There is a high-level diff() API that the command-line tool uses (that you can tweak the behavior of), and there are also lower level APIs that you can use in projects that perhaps want to do something more complicated than just visualization in PDFs.
You can find the musicdiff API documentation here.
If you use this work in any research, please cite the relevant paper:
@inproceedings{foscarin2019diff,
title={A diff procedure for music score files},
author={Foscarin, Francesco and Jacquemard, Florent and Fournier-S’niehotta, Raphael},
booktitle={6th International Conference on Digital Libraries for Musicology},
pages={58--64},
year={2019}
}
The paper is freely available here.
Many thanks to Francesco Foscarin for allowing me to use his music-score-diff code, and for continuing to work with and advise me on this project.
The MIT License (MIT) Copyright (c) 2022, 2023 Francesco Foscarin, Greg Chapman
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