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A Two-Step Approach for Classifying Music Genre on the Strength of AHP Weighted Musical Features

Author

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  • Yu-Tso Chen

    (Department of Information Management, National United University, Miaoli 36003, Taiwan)

  • Chi-Hua Chen

    (College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China)

  • Szu Wu

    (Department of Information Management and Finance, National Chiao Tung University, Hsinchu 30010, Taiwan)

  • Chi-Chun Lo

    (Department of Information Management and Finance, National Chiao Tung University, Hsinchu 30010, Taiwan)

Abstract

Music is a series of harmonious sounds well arranged by musical elements including rhythm, melody, and harmony (RMH). Since music digitalization has resulted in a wide variety of new musical applications used in daily life, the use of music genre classification (MGC), especially MGC automation, is increasingly playing a key role in the development of novel musical services. However, achieving satisfactory performance of MGC automation is a practical challenge. This paper proposes a two-step approach for music genre classification (called TSMGC) on the strength of analytic hierarchy process (AHP) weighted musical features. Compared with other MGC approaches, the TSMGC has three strong points for better performance: (1) various musical features extracted from the RMH and the calculated entropy are comprehensively considered, (2) the weight of features and their impact values determined by AHP are applied on the basis of the Exponential Distribution function, (3) music can be accurately categorized into a main-class and further sub-classes through a two-step classification process. According to the conducted experiment, the result exhibits an accuracy rate of 87%, which demonstrates the potential for the proposed TSMGC method to meet the emerging needs of MGC automation.

Suggested Citation

  • Yu-Tso Chen & Chi-Hua Chen & Szu Wu & Chi-Chun Lo, 2018. "A Two-Step Approach for Classifying Music Genre on the Strength of AHP Weighted Musical Features," Mathematics, MDPI, vol. 7(1), pages 1-17, December.
    • Handle: RePEc:gam:jmathe:v:7:y:2018:i:1:p:19-:d:193126
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    References listed on IDEAS

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    1. Diego Raphael Amancio & Cesar Henrique Comin & Dalcimar Casanova & Gonzalo Travieso & Odemir Martinez Bruno & Francisco Aparecido Rodrigues & Luciano da Fontoura Costa, 2014. "A Systematic Comparison of Supervised Classifiers," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-14, April.
    2. Chi-Hua Chen & Hui-Fei Lin & Hsu-Chia Chang & Ping-Hsien Ho & Chi-Chun Lo, 2013. "An Analytical Framework of a Deployment Strategy for Cloud Computing Services: A Case Study of Academic Websites," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-14, September.
    3. Saaty, Thomas L., 1990. "How to make a decision: The analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 48(1), pages 9-26, September.
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