A model of music perceptual theory based on Markov chains

  • Ru Wen
  • , Kai Chen
  • , Yilin Zhang
  • , Wenmin Huang
  • , Jiyuan Tian
  • , Kuan Xu
  • , Jiang Wu

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

2 Scopus citations

Abstract

Music perceptions can be regarded as the expectancies for which the brain processes temporal statistics to predict future rhythms. The different patterns of the expectancy streams represent the different styles of music. In this paper, we present a model for music style recognition based on a music cognitive theory combined with machine learning approach. First, we establish a Markov chain with eight states where each state represents a certain composition mode derived from the Implication-Realization (IR) theory. Then we use a clustering method to detect music styles hidden in compositions. The results are identical to the conclusions in musicology, which confirms the effectiveness of our method.

Original languageEnglish
Title of host publicationProceedings of the 30th Chinese Control and Decision Conference, CCDC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1099-1105
Number of pages7
ISBN (Electronic)9781538612439
DOIs
StatePublished - 6 Jul 2018
Event30th Chinese Control and Decision Conference, CCDC 2018 - Shenyang, China
Duration: 9 Jun 201811 Jun 2018

Publication series

NameProceedings of the 30th Chinese Control and Decision Conference, CCDC 2018

Conference

Conference30th Chinese Control and Decision Conference, CCDC 2018
Country/TerritoryChina
CityShenyang
Period9/06/1811/06/18

Keywords

  • Implication-Realization theory
  • Markov chain
  • Music style analysis
  • clustering
  • machine learning

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