Music self-similarity modeling using augmented nonnegative matrix factorization of block and stripe patterns

Joonas Kauppinen, Anssi Klapuri, Tuomas Virtanen
2013 2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics  
Self-similarity matrices have been widely used to analyze the sectional form of music signals, e.g. enabling the detection of parts such as verse and chorus in popular music. Two main types of structures often appear in self-similarity matrices: rectangular blocks of high similarity and diagonal stripes off the main diagonal that represent recurrent sequences. In this paper, we introduce a novel method to model both the block and stripe-like structures in selfsimilarity matrices and to pull
more » ... apart from each other. The model is an extension of the nonnegative matrix factorization, for which we present multiplicative update rules based on the generalized Kullback-Leibler divergence. The modeling power of the proposed method is illustrated with examples, and we demonstrate its application to the detection of sectional boundaries in music. Index Terms-Music structure analysis, nonnegative matrix factorization, self-similarity * Joonas Kauppinen is funded by Tampere Doctoral Programme in Information Science and Engineering.
doi:10.1109/waspaa.2013.6701855 dblp:conf/waspaa/KauppinenKV13 fatcat:3ax3tyru2jhjha2qh3ieb2ikmu