Measuring musical rhythm similarity: Statistical features versus transformation methods

J. F. Beltran, X. Liu, N. Mohanchandra, G. T. Toussaint

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Two approaches to measuring the similarity between symbolically notated musical rhythms are compared with human judgments of perceived similarity. The first is the edit-distance, a popular transformation method, applied to the rhythm sequences. The second works on the histograms of the inter-onset- intervals (IOIs) of these rhythm sequences. Furthermore, two methods of dealing with the histograms are also compared: the Mallows distance, and the employment of a group of standard statistical features. The results provide further evidence from the aural domain, that transformation methods are superior to feature-based methods for predicting human judgments of similarity. Furthermore, the results also support the hypothesis that statistical features applied to the histograms of the rhythms are better than music-theoretical structural features applied to the rhythms themselves.

Original languageEnglish (US)
Title of host publicationICPRAM 2013 - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods
Pages595-598
Number of pages4
StatePublished - 2013
Event2nd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2013 - Barcelona, Spain
Duration: Feb 15 2013Feb 18 2013

Publication series

NameICPRAM 2013 - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods

Other

Other2nd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2013
CountrySpain
CityBarcelona
Period2/15/132/18/13

Keywords

  • Edit distance
  • Inter-onset interval histograms
  • Mallows distance
  • Mantel test
  • Music information retrieval
  • Musical rhythm
  • Pattern recognition
  • Similarity measures
  • Statistical features
  • Transformations

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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