Automatic classification of musical genres using inter-genre similarity

Ulaş Baǧci*, Engin Erzin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

Musical genre classification is an essential tool for music information retrieval systems and it has potential to become a highly demanded application in various media platforms. Two important problems of the automatic musical genre classification are feature extraction and classifier design. In this letter, we propose two novel classifiers using inter-genre similarity (IGS) modeling and investigate the use of dynamic timbral texture features in order to improve automatic musical genre classification performance. Inter-genre similarity is modeled over hard-to-classify samples of the musical genre feature space. In the classification, samples within inter-genre similarity class are eliminated to reduce inter-genre confusion and to improve genre classification performance. Experimental results show that the proposed classifiers provide better classification rates than the existing methods.

Original languageEnglish (US)
Pages (from-to)521-524
Number of pages4
JournalIEEE Signal Processing Letters
Volume14
Issue number8
DOIs
StatePublished - Aug 2007
Externally publishedYes

Keywords

  • Inter-genre similarity (IGS) modeling
  • Mel-frequency cepstral coefficients (MFCC)
  • Musical genre classification

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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