From Genre Classification to Rhythm Similarity: Computational and Musicological Insights

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

Research output: Contribution to journalArticlepeer-review


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
Issue number1
StatePublished - Jan 2 2015


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

ASJC Scopus subject areas

  • Visual Arts and Performing Arts
  • Music


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