Unsupervised discovery of temporal structure in music

Ron J. Weiss, Juan Pablo Bello

Research output: Contribution to journalArticlepeer-review


We describe a data-driven algorithm for automatically identifying repeated patterns in music which analyzes a feature matrix using shift-invariant probabilistic latent component analysis. We utilize sparsity constraints to automatically identify the number of patterns and their lengths, parameters that would normally need to be fixed in advance, as well as to control the structure of the decomposition. The proposed analysis is applied to beat-synchronous chromagrams in order to concurrently extract recurrent harmonic motifs and their locations within a song. We demonstrate how the analysis can be used to accurately identify riffs in popular music and explore the relationship between the derived parameters and a song's underlying metrical structure. Finally, we show how this analysis can be used for long-term music 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)
Article number5753914
Pages (from-to)1240-1251
Number of pages12
JournalIEEE Journal on Selected Topics in Signal Processing
Issue number6
StatePublished - Oct 2011


  • Convolutive non-negative matrix factorization (NMF)
  • Music structure analysis
  • Sparse priors

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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