Identifying repeated patterns in music using sparse convolutive non-negative matrix factorization

Ron J. Weiss, Juan Pablo Bello

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We describe an unsupervised, data-driven, method for automatically identifying repeated patterns in music by analyzing a feature matrix using a variant of sparse convolutive non-negative matrix factorization. We utilize sparsity constraints to automatically identify the number of patterns and their lengths, parameters that would normally need to be fixed in advance. The proposed analysis is applied to beatsynchronous chromagrams in order to concurrently extract repeated harmonic motifs and their locations within a song. Finally, we show how this analysis can be used for longterm structure segmentation, resulting in an algorithm that is competitive with other state-of-the-art segmentation algorithms based on hidden Markov models and self similarity matrices.

Original languageEnglish (US)
Title of host publicationProceedings of the 11th International Society for Music Information Retrieval Conference, ISMIR 2010
Pages123-128
Number of pages6
StatePublished - 2010
Event11th International Society for Music Information Retrieval Conference, ISMIR 2010 - Utrecht, Netherlands
Duration: Aug 9 2010Aug 13 2010

Publication series

NameProceedings of the 11th International Society for Music Information Retrieval Conference, ISMIR 2010

Other

Other11th International Society for Music Information Retrieval Conference, ISMIR 2010
CountryNetherlands
CityUtrecht
Period8/9/108/13/10

ASJC Scopus subject areas

  • Music
  • Information Systems

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  • Cite this

    Weiss, R. J., & Bello, J. P. (2010). Identifying repeated patterns in music using sparse convolutive non-negative matrix factorization. In Proceedings of the 11th International Society for Music Information Retrieval Conference, ISMIR 2010 (pp. 123-128). (Proceedings of the 11th International Society for Music Information Retrieval Conference, ISMIR 2010).