Measuring musical rhythm similarity: Edit distance versus minimum-weight many-to-many matchings

Godfried T. Toussaint, Seung Man Oh

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

Abstract

Musical rhythms are represented as binary symbol sequences of sounded and silent pulses of unit-duration. A measure of distance (dissimilarity) between a pair of rhythms commonly used in music information retrieval, music perception, and musicology is the edit (Levenshtein) distance, defined as the minimum number of symbol insertions, deletions, and substitutions needed to transform one rhythm into the other. A measure of distance often used in object recognition is the minimum-weight many-to-many matching distance between the object’s features. These two approaches are compared empirically, in terms of how well they predict human judgments of musical rhythm similarity, using a real-world family of Middle-Eastern rhythms.

Original languageEnglish (US)
Title of host publicationProceedings of the 2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016
EditorsHamid R. Arabnia, David de la Fuente, Roger Dziegiel, Elena B. Kozerenko, Peter M. LaMonica, Raymond A. Liuzzi, Jose A. Olivas, Todd Waskiewicz, George Jandieri, Ashu M.G. Solo, Fernando G. Tinetti
PublisherCSREA Press
Pages186-189
Number of pages4
ISBN (Electronic)1601324383, 9781601324382
StatePublished - 2016
Event2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016 - Las Vegas, United States
Duration: Jul 25 2016Jul 28 2016

Publication series

NameProceedings of the 2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016

Conference

Conference2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016
CountryUnited States
CityLas Vegas
Period7/25/167/28/16

Keywords

  • Edit distance
  • Hungarian algorithm
  • Many-to-many matchings
  • Musical rhythm
  • Perception
  • Similarity measures

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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