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In this paper, we will propose an automatic music genre classification approach based on long-term modulation spectral analysis on the static and transitional information of spectral (OSC and MPEG-7 NASE) as well as cepstral (MFCC) features. An information fusion approach which integrates both feature level fusion and decision level combination is employed to improve the classification accuracy. Experiments...
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...
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...
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