A Comparative Study on Content-Based Music Genre Classification

Tao Li, Mitsunori Ogihara, Qi Li

Research output: Contribution to journalConference article

266 Scopus citations

Abstract

Content-based music genre classification is a fundamental component of music information retrieval systems and has been gaining importance and enjoying a growing amount of attention with the emergence of digital music on the Internet. Currently little work has been done on automatic music genre classification, and in addition, the reported classification accuracies are relatively low. This paper proposes a new feature extraction method for music genre classification, DWCHs 1. DWCHs capture the local and global information of music signals simultaneously by computing histograms on their Daubechies wavelet coefficients. Effectiveness of this new feature and of previously studied features are compared using various machine learning classification algorithms, including Support Vector Machines and Linear Discriminant Analysis. It is demonstrated that the use of DWCHs significantly improves the accuracy of music genre classification.

Original languageEnglish (US)
Pages (from-to)282-289
Number of pages8
JournalSIGIR Forum (ACM Special Interest Group on Information Retrieval)
Issue numberSPEC. ISS.
StatePublished - Dec 1 2003
EventProceedings of the Twenty-Sixth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2003 - Toronto, Ont., Canada
Duration: Jul 28 2003Aug 1 2003

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Keywords

  • Feature extraction
  • Multi-class classification
  • Music Genre Classification
  • Wavelet coefficients histogram

ASJC Scopus subject areas

  • Management Information Systems
  • Hardware and Architecture

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