musiscape

musiscape is a Python toolbox that analyses a folder tree of music as a collection: albums, similarity, outliers, categories. Point it at a folder of audio files—each subfolder counts as an album—and it renders figures, tables and thumbnails that let you compare many tracks at a glance.

Install

pip install musiscape

Quickstart

musiscape report ~/Music/my-collection

One command runs the whole pipeline and writes analysis/README.md next to your music: an album table, a similarity landscape, self-explaining categories, and the corpus extremes.

Per-album fingerprint bars from a synthetic demo collection

Commands

Command	What it does
`probe`	list albums and tracks, no analysis
`extract`	per-track features, cached in `features.json`
`fingerprint`	per-album profile bars
`landscape`	PCA similarity map and album-affinity matrix
`categorize`	k-means categories with named signatures
`report`	everything above, gathered into `analysis/README.md`
`thumbnails`	one visual card per track (`--style`, seventeen styles)
`poster`	the whole collection as one image (`--style vinyl` for discs)
`sonic`	a ~12-second audio summary per track, plus album medleys
`segment`	finds the songs in a concert recording and writes one file per song
`figures`	labelled chromagram and tempogram per track, at any pixel width
`pdf`	one PDF: a summary table, then a page of figures per track

A live recording is not a collection until it is cut into songs, which is what segment is for (video files included — concerts usually arrive as video):

musiscape segment ~/video/concert -o ~/video/concert/analysis
musiscape report  ~/video/concert/analysis/songs

Common options: -o sets the output folder (default <root>/analysis), --workers N parallelises, --duration S analyses only the first S seconds per track, and -k fixes the number of categories.

Learn more

Documentation—guides, an illustrated gallery of the visualisation styles, and the API reference
Wiki—methodology notes and a case study

The toolbox is meant for quick visualisations and overviews. Combine it with listening.

Related toolboxes

These toolboxes come out of the fourMs lab at the University of Oslo. They are separate packages with separate release cycles, but they are built to be used together and share several implementations, so a measure computed in one agrees with the same measure computed in another.

Musical Gestures Toolbox (musicalgestures)—video and audio: motiongrams, videograms, and motion analysis from ordinary video files
ambiscape—soundscapes: the sonic ambience of a place; musiscape reuses its circular statistics and Schaeffer typology machinery
micromotion—human micromotion: quantity of motion from optical markers, accelerometers, respiration belts and force plates

Licence and credit

MIT licence. musiscape is developed as part of the AMBIENT project at RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo.

Citing

Cite the CONCEPT DOI, which always resolves to the newest version:

Jensenius, A. R. (2026). musiscape: analysis of music collections (Version 0.5.0) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.21948999

Where the exact behaviour matters, cite the version you ran instead. Version 0.5.0 is https://doi.org/10.5281/zenodo.21949000.

CITATION.cff in this repository carries the same information in machine-readable form.

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