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Dynamic nonparametric bayesian models for analysis of music

Publication ,  Journal Article
Ren, L; Dunson, D; Lindroth, S; Carin, L
Published in: Journal of the American Statistical Association
June 1, 2010

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.

Duke Scholars

Published In

Journal of the American Statistical Association

DOI

ISSN

0162-1459

Publication Date

June 1, 2010

Volume

105

Issue

490

Start / End Page

458 / 472

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1603 Demography
  • 1403 Econometrics
  • 0104 Statistics
 

Citation

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ICMJE
MLA
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Ren, L., Dunson, D., Lindroth, S., & Carin, L. (2010). Dynamic nonparametric bayesian models for analysis of music. Journal of the American Statistical Association, 105(490), 458–472. https://doi.org/10.1198/jasa.2009.ap08497
Ren, L., D. Dunson, S. Lindroth, and L. Carin. “Dynamic nonparametric bayesian models for analysis of music.” Journal of the American Statistical Association 105, no. 490 (June 1, 2010): 458–72. https://doi.org/10.1198/jasa.2009.ap08497.
Ren L, Dunson D, Lindroth S, Carin L. Dynamic nonparametric bayesian models for analysis of music. Journal of the American Statistical Association. 2010 Jun 1;105(490):458–72.
Ren, L., et al. “Dynamic nonparametric bayesian models for analysis of music.” Journal of the American Statistical Association, vol. 105, no. 490, June 2010, pp. 458–72. Scopus, doi:10.1198/jasa.2009.ap08497.
Ren L, Dunson D, Lindroth S, Carin L. Dynamic nonparametric bayesian models for analysis of music. Journal of the American Statistical Association. 2010 Jun 1;105(490):458–472.
Journal cover image

Published In

Journal of the American Statistical Association

DOI

ISSN

0162-1459

Publication Date

June 1, 2010

Volume

105

Issue

490

Start / End Page

458 / 472

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1603 Demography
  • 1403 Econometrics
  • 0104 Statistics