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Proceedings Paper

Music genre classification using temporal domain features
Author(s): Yu Shiu; C.-C. Jay Kuo
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Paper Abstract

Music genre provides an efficient way to index songs in the music database, and can be used as an effective means to retrieval music of a similar type, i.e. content-based music retrieval. In addition to other features, the temporal domain features of a music signal are exploited so as to increase the classification rate in this research. Three temporal techniques are examined in depth. First, the hidden Markov model (HMM) is used to emulate the time-varying properties of music signals. Second, to further increase the classification rate, we propose another feature set that focuses on the residual part of music signals. Third, the overall classification rate is enhanced by classifying smaller segments from a test material individually and making decision via majority voting. Experimental results are given to demonstrate the performance of the proposed techniques.

Paper Details

Date Published: 25 October 2004
PDF: 12 pages
Proc. SPIE 5601, Internet Multimedia Management Systems V, (25 October 2004); doi: 10.1117/12.571369
Show Author Affiliations
Yu Shiu, Univ. of Southern California (United States)
C.-C. Jay Kuo, Univ. of Southern California (United States)

Published in SPIE Proceedings Vol. 5601:
Internet Multimedia Management Systems V
John R. Smith; Tong Zhang; Sethuraman Panchanathan, Editor(s)

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