Mel Frequency Cepstral Coefficients for Music Modeling

We examine in some detail Mel Frequency Cepstral Coefficients (MFCCs) - the dominant features used for speech recognition - and investigate their applicability to modeling music. In particular, we examine two of the main assumptions of the process of forming MFCCs: the use of the Mel frequency scale to model the spectra; and the use of the Discrete Cosine Transform (DCT) to decorrelate the Mel-spectral vectors. We examine the first assumption in the context of speech/music discrimination. Our results show that the use of the Mel scale for modeling music is at least not harmful for this problem, although further experimentation is needed to verify that this is the optimal scale in the general case. We investigate the second assumption by examining the basis vectors of the theoretically optimal transform to decorrelate music and speech spectral vectors. Our results demonstrate that the use of the DCT to decorrelate vectors is appropriate for both speech and music spectra. MFCCs for Musi...

• Type: Conference
• Year: 2000
• Where: ISMIR
• Authors: Beth Logan
• Keywords: ISMIR 2000, Mel Frequency Cepstral Coefficients, Music, Speech Recognition