Sequence Representation of Music Structure Using Higher-Order Similarity Matrix and Maximum-Likelihood Approach
نویسنده
چکیده
In this paper, we present a novel method for the automatic estimation of the structure of music tracks using a sequence representation. A set of timbre-related (MFCC and Spectral Contrast) and pitch-related (Pitch Class Profile) features are first extracted from the signal leading to three similarity matrices which are then combined. We then introduce the use of higher-order (2nd and 3rd order) similarity matrices in order to reinforce the diagonals corresponding to common repetitions and reduce the background noise. Segments are then detected and a maximum-likelihood approach is proposed in order to derive simultaneously the underlying sequence representation of the music track and the most representative segment of each sequence. The proposed method is evaluated positively on the MPEG-7 “melody repetition” test set.
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