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A Study on the Robust Content-Based Musical Genre Classification System Using Multi-Feature Clustering  

Yoon Won-Jung (Dept. of Computer Science and Statistics, Dankook University)
Lee Kang-Kyu (Dept. of Computer Science and Statistics, Dankook University)
Park Kyu-Sik (Dept. of Computer Science and Statistics, Dankook University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.42, no.3, 2005 , pp. 115-120 More about this Journal
Abstract
In this paper, we propose a new robust content-based musical genre classification algorithm using multi-feature clustering(MFC) method. In contrast to previous works, this paper focuses on two practical issues of the system dependency problem on different input query patterns(or portions) and input query lengths which causes serious uncertainty of the system performance. In order to solve these problems, a new approach called multi-feature clustering(MFC) based on k-means clustering is proposed. To verify the performance of the proposed method, several excerpts with variable duration were extracted from every other position in a queried music file. Effectiveness of the system with MFC and without MFC is compared in terms of the classification accuracy. It is demonstrated that the use of MFC significantly improves the system stability of musical genre classification performance with higher accuracy rate.
Keywords
Multi-Feature Clustering(MFC); Music Genre Classification; query patterns; query length; system stability;
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