vega-mir: An information-theoretic Python toolkit for symbolic music, with applications to harmonic graphs and rubato spectra
Fred Jalbert-Desforges

TL;DR
vega-mir is an open-source Python toolkit that provides nine information-theoretic and statistical metrics for symbolic music analysis, demonstrated through case studies on harmonic graphs and rubato spectra.
Contribution
It introduces vega-mir, a comprehensive library with validated metrics for symbolic music analysis, and applies it to novel case studies not addressed in prior work.
Findings
PageRank of gravity-centre node correlates with marginal KL distance (rho=0.61).
Gould's rubato has a median dominant period of 66 beats, contrary to the low-rubato stereotype.
Four metrics are validated against analytic anchors and tested on an 8-composer dataset.
Abstract
We present vega-mir, an open-source Python library that bundles nine information-theoretic and statistical metrics for the analysis of symbolic music corpora behind a small, tested, citable API, and demonstrates two of them at corpus scale in case studies not addressed by the upstream Cygnus paper. Of the nine metrics, three (Shannon entropy, Kullback-Leibler divergence, Zipfian fits) were deployed in the companion Cygnus arXiv preprint; two (network analysis on chord-transition graphs and spectral analysis of rubato curves) are deployed in full case studies here; the four remaining (multi-dimensional Gini, chi-squared stationarity, Higuchi fractal dimension, interval distribution) are validated against analytic anchors and exercised as sanity checks on a bundled 8-composer dataset. The two case studies yield two main observations. First, on the fourteen MAESTRO composers with N >= 10…
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