Mark R H Gotham
Rainer Kleinertz
Christof Weiss
Meinard Müller
Stephanie Klauk
2021-11-07
This paper uses the emerging provision of human harmonic analyses to assess how reliably we can map from knowing only when chords and keys change to a full identification of what those chords and keys are. We do this with a simple implementation of pitch class profile matching methods, partly to provide a benchmark score against which to judge the performance of less readily interpretable machine learning systems, many of which explicitly separate these when and what tasks and provide performance evaluation for these separate stages. Additionally, as this 'oracle'-style, 'perfect' segmentation information will not usually be available in practice, we test the sensitivity of these methods to slight modifications in the position of segment boundaries by introducing deliberate errors. This study examines several corpora. The focus on is symbolic data, though we include one audio dataset for comparison. The code and corpora (of symbolic scores and analyses) are available within: https://github.com/MarkGotham/When-in-Rome
https://doi.org/10.5281/zenodo.5624493
oai:zenodo.org:5624493
ISMIR
https://zenodo.org/communities/ismir
https://doi.org/10.5281/zenodo.5624492
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
ISMIR 2021, International Society for Music Information Retrieval Conference, Online, November 7-12, 2021
What if the 'When' Implies the 'What'?: Human harmonic analysis datasets clarify the relative role of the separate steps in automatic tonal analysis
info:eu-repo/semantics/conferencePaper