Title :
Automatic Music Genre Classification using Modulation Spectral Contrast Feature
Author :
Lee, Chang-Hsing ; Shih, Jau-Ling ; Yu, Kun-Ming ; Su, Jung-Mau
Author_Institution :
Chung Hua Univ., Hsinchu
Abstract :
In this paper, we proposed a novel feature, called octave-based modulation spectral contrast (OMSC), for music genre classification. OMSC is extracted from long-term modulation spectrum analysis to represent the time-varying behavior of music signals. Experimental results have shown that OMSC outperforms MFCC and OSC. If OMSC is integrated with MFCC and OSC, the classification accuracy is 84.03% for seven music genre classification.
Keywords :
audio signal processing; electronic music; feature extraction; modulation; signal classification; spectral analysis; OMSC extraction; automatic music genre classification; digital music; octave-based modulation spectrum analysis; spectral contrast feature; time-varying behavior; Cepstral analysis; Data mining; Feature extraction; Histograms; Linear discriminant analysis; Mel frequency cepstral coefficient; Multiple signal classification; Speech; Support vector machine classification; Support vector machines;
Conference_Titel :
Multimedia and Expo, 2007 IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
1-4244-1016-9
Electronic_ISBN :
1-4244-1017-7
DOI :
10.1109/ICME.2007.4284622
