From Genre Classification to Rhythm Similarity: Computational and Musicological Insights

Tlacael Miguel Esparza, Juan Pablo Bello, Eric J. Humphrey

Research output: Contribution to journalArticle

Abstract

Traditionally, the development and validation of computational measures of rhythmic similarity in music relies on proxy classification tasks, often equating rhythm similarity to genre. In this paper, we perform a comprehensive, cross-disciplinary exploration of the classification performance of a state-of-the-art system for rhythm similarity. By synthesizing the methods of quantitative analysis with a musicological perspective, detailed insight is gained into the various facets that affect system behaviour, consisting of three main areas: rhythmic sensitivities of a given feature representation, idiosyncrasies of the data used for evaluation, and the tenuous relationship between rhythmic similarity and genre. Through this study, we provide perspective on gauging the abilities of a computational system beyond classification accuracy, as well as a deeper understanding of system design and evaluation methodology as a musically meaningful exercise.

Original languageEnglish (US)
Pages (from-to)39-57
Number of pages19
JournalJournal of New Music Research
Volume44
Issue number1
DOIs
StatePublished - Jan 2 2015

Keywords

  • audio analysis
  • information retrieval
  • machine learning
  • music analysis

ASJC Scopus subject areas

  • Visual Arts and Performing Arts
  • Music

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