Measuring musical rhythm similarity: Statistical features versus transformation methods

Juan F. Beltran, Xiaohua Liu, Nishant Mohanchandra, Godfried T. Toussaint

Research output: Contribution to journalArticle

Abstract

Two approaches to measuring the similarity between symbolically notated musical rhythms are compared with each other and with human judgments of perceived similarity. The first is the edit-distance, a popular transformation method, applied to the symbolic rhythm sequences. The second approach employs the histograms of the inter-onset-intervals (IOIs) calculated from the rhythms. Furthermore, two methods for dealing with the histograms are also compared. The first utilizes the Mallows distance, a transformation method akin to the Earth-Movers distance popular in computer vision, and the second extracts a group of standard statistical features, used in music information retrieval, from the IOI-histograms. The measures are compared using four contrastive musical rhythm data sets by means of statistical Mantel tests that compute correlation coefficients between the various dissimilarity matrices. The results provide evidence from the aural domain, that transformation methods such as the edit distance are superior to feature-based methods for predicting human judgments of similarity. The evidence also supports the hypothesis that IOI-histogram-based methods are better than music-theoretical structural features computed from the rhythms themselves, provided that the rhythms do not share identical IOI histograms.

Original languageEnglish (US)
Article number1550009
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume29
Issue number2
DOIs
StatePublished - Mar 25 2015

Keywords

  • Mantel test
  • Musical rhythm
  • edit distance
  • inter-onset interval histograms
  • mallows distance
  • music information retrieval
  • similarity measures
  • symbolic music notation
  • transformations

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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