Title :
Music genre classification using novel features and a weighted voting method
Author :
Jang, Dalwon ; Jin, Minho ; Yoo, Chang D.
Author_Institution :
Div. of EE, KAIST, Daejeon
fDate :
June 23 2008-April 26 2008
Abstract :
This paper proposes a novel music genre classification system based on two novel features and a weighted voting. 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 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.
Keywords :
audio signal processing; music; signal classification; beat strength; consecutive time segments; majority voting; modulation spectral crest measure; modulation spectral flatness measure; music genre classification; octave-based modulation spectral contrast; short feature vector; statistical method; time-varying behavior; weighted voting method; Discrete Fourier transforms; Histograms; Internet; Mel frequency cepstral coefficient; Multiple signal classification; Shape measurement; Spatial databases; Statistical analysis; Statistics; Voting;
Conference_Titel :
Multimedia and Expo, 2008 IEEE International Conference on
Conference_Location :
Hannover
Print_ISBN :
978-1-4244-2570-9
Electronic_ISBN :
978-1-4244-2571-6
DOI :
10.1109/ICME.2008.4607700