Music genre classification using temporal domain features

Yu Shiu; C.-C. Jay Kuo

doi:10.1117/12.571369

25 October 2004 Music genre classification using temporal domain features

Yu Shiu, C.-C. Jay Kuo

Proceedings Volume 5601, Internet Multimedia Management Systems V; (2004) https://doi.org/10.1117/12.571369
Event: Optics East, 2004, Philadelphia, Pennsylvania, United States

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.

Citation Download Citation

Yu Shiu and C.-C. Jay Kuo "Music genre classification using temporal domain features", Proc. SPIE 5601, Internet Multimedia Management Systems V, (25 October 2004); https://doi.org/10.1117/12.571369

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available