In this paper, we will propose an automatic music genre classification approach based on long-term modulation spectral analysis of spectral (OSC and MPEG-7 NASE) as well as cepstral (MFCC) features. Modulation spectral analysis of every feature value will generate a corresponding modulation spectrum and all the modulation spectra can be collected to form a modulation spectrogram which exhibits the time-varying or rhythmic information of music signals. Each modulation spectrum is then decomposed into several logarithmically-spaced modulation subbands. The modulation spectral contrast (MSC) and modulation spectral valley (MSV) are then computed from each modulation subband. Effective and compact features are generated from statistical aggregations of the MSCs and MSVs of all modulation subbands. An information fusion approach which integrates both feature level fusion method and decision level combination method is employed to improve the classification accuracy. Experiments conducted on two different music datasets have shown that our proposed approach can achieve higher classification accuracy than other approaches with the same experimental setup.

In this paper, an automatic music genre classification approach which integrates the features derived from static and transitional information of cepstral (MFCC) and spectral (OSC) features will be proposed. MFCC and OSC capture the characteristics of one audio frame. Therefore, the transitional information, including delta-MFCC, delta-OSC, delta-delta-MFCC, and delta-delta-OSC, are then extracted and combined with MFCC and OSC to improve the classification accuracy. Two information fusion techniques, including feature level fusion and decision level fusion, are developed to combine the extracted feature vectors. Experiments conducted on the music database employed in the ISMIR2004 Audio Description Contest have shown that the proposed approach can achieve a classification accuracy of 84.23%, which is better than the winner of the ISMIR2004 Music Genre Classification Contest.