Automatic classification of korean traditional music using robust multi-feature clustering

Authors:
Kyu-Sik Park;Youn-Ho Cho;Sang-Hun Oh
Affiliations:
Dankook University Division of Information and Computer Science, Seoul, Korea;Dankook University Division of Information and Computer Science, Seoul, Korea;Dankook University Division of Information and Computer Science, Seoul, Korea
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part II
Year:
2005

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Abstract

An automatic classification system of Korean traditional music is proposed using robust multi-feature clustering method. The system accepts query sound and automatically classifies input query into one of the six Korean traditional music categories. This paper focuses on the feature optimization method to alleviate system uncertainty problem due to the different query patterns and lengths, and consequently increase the system stability and performance. In order to fit this needs, a robust feature optimization method called multi-feature clustering (MFC) based on VQ and SFS feature selection is proposed. Several pattern classification algorithms are tested and compared in terms of the system stability and classification accuracy.