Dynamic nonparametric bayesian models for analysis of music

Lu Ren, David Dunson, Scott Lindroth, Lawrence Carin

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

The dynamic hierarchical Dirichlet process (dHDP) is developed to model complex sequential data, with a focus on audio signals from music. The music is represented in terms of a sequence of discrete observations, and the sequence is modeled using a hidden Markov model (HMM) with time-evolving parameters. The dHDP imposes the belief that observations that are temporally proximate are more likely to be drawn from HMMs with similar parameters, while also allowing for "innovation" associated with abrupt changes in the music texture. The sharing mechanisms of the time-evolving model are derived, and for inference a relatively simple Markov chain Monte Carlo sampler is developed. Segmentation of a given musical piece is constituted via the model inference. Detailed examples are presented on several pieces, with comparisons to other models. The dHDP results are also compared with a conventional music-theoretic analysis. All the supplemental materials used by this paper are available online. © 2010 American Statistical Association.
Original languageEnglish (US)
Pages (from-to)458-472
Number of pages15
JournalJournal of the American Statistical Association
Volume105
Issue number490
DOIs
StatePublished - Jun 1 2010
Externally publishedYes

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