Pattern discovery from audio recordings by Variable Markov Oracle: A music information dynamics approach

Cheng I. Wang, Shlomo Dubnov

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

9 Scopus citations

Abstract

In this paper, a framework for automatic pattern discovery within an audio recording is proposed. The concept of the proposed framework stems from music information dynamics and is realized by Variable Markov Oracle. Music information dynamics is the research area focusing on information theoretic measures describing musical structure and is thus closely related to the field of music pattern discovery. Variable Markov Oracle is a data structure that provides both fast retrieval of repeated sub-clips from a signal and efficient calculation of music information dynamics measures. Evaluation of the proposed framework is performed on the JKU Patterns Development Dataset with significantly improved performance of the current state of the art.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages683-687
Number of pages5
ISBN (Electronic)9781467369978
DOIs
StatePublished - 4 Aug 2015
Externally publishedYes
Event40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Australia
Duration: 19 Apr 201424 Apr 2014

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2015-August
ISSN (Print)1520-6149

Conference

Conference40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
Country/TerritoryAustralia
CityBrisbane
Period19/04/1424/04/14

Keywords

  • Data structures
  • Music information retrieval
  • Pattern analysis
  • Variable Markov Oracle

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