Mining polyphonic repeating patterns from music data using bit-string based approaches

Shih Chuan Chiu*, Man Kwan Shan, Jiun-Long Huang, Hua Fu Li

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

Mining repeating patterns from music data is one of the most interesting issues of multimedia data mining. However, less work are proposed for mining polyphonic repeating patterns. Hence, two efficient algorithms, A-PRPD (Apriori-based Polyphonic Repeating Pattern Discovery) and T-PRPD (Tree-based Polyphonic Repeating Pattern Discovery), are proposed to discover polyphonic repeating patterns from music data. Furthermore, a bit-string method is developed for improving the efficiency of the proposed algorithms. Experimental results show that the proposed algorithms, A-PRPD and T-PRPD, are both effective and efficient methods for mining polyphonic repeating patterns from synthetic music data and real data.

Original languageEnglish
Title of host publicationProceedings - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009
Pages1170-1173
Number of pages4
DOIs
StatePublished - 20 Nov 2009
Event2009 IEEE International Conference on Multimedia and Expo, ICME 2009 - New York, NY, United States
Duration: 28 Jun 20093 Jul 2009

Publication series

NameProceedings - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009

Conference

Conference2009 IEEE International Conference on Multimedia and Expo, ICME 2009
CountryUnited States
CityNew York, NY
Period28/06/093/07/09

Keywords

  • Multimedia data mining
  • Music data mining
  • Polyphonic repeating patterns
  • Repeating patterns

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