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Music information retrieval using novel features and a weighted voting method

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2 Author(s)
Dalwon Jang ; Sch. of EECS, KAIST, Daejeon, South Korea ; Yoo, C.D.

This paper proposes a novel music information retrieval system (music genre and music mood classification system) based on two novel features and a weighted voting method. The proposed features, modulation spectral flatness measure (MSFM) and modulation spectral crest measure (MSCM), represent the time-varying behavior of a music and indicate the beat strength. The weighted voting method determines the music genre or the music mood by summarizing the classification results of consecutive time segments. Experimental results show that the proposed features give more accurate classification results when combined with traditional features than the octave-based modulation spectral contrast (OMSC) does in spite of short feature vector and that the weighted voting is more effective than statistical method and majority voting.

Published in:

Industrial Electronics, 2009. ISIE 2009. IEEE International Symposium on

Date of Conference:

5-8 July 2009