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dc.contributor.author Ren, L
dc.contributor.author Dunson, D
dc.contributor.author Lindroth, S
dc.contributor.author Carin, L
dc.date.accessioned 2011-06-21T17:30:30Z
dc.date.issued 2010-06-01
dc.identifier.citation Journal of the American Statistical Association, 2010, 105 (490), pp. 458 - 472
dc.identifier.issn 0162-1459
dc.identifier.uri http://hdl.handle.net/10161/4397
dc.description.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.
dc.format.extent 458 - 472
dc.language.iso en_US en_US
dc.relation.ispartof Journal of the American Statistical Association
dc.relation.isversionof 10.1198/jasa.2009.ap08497
dc.title Dynamic nonparametric bayesian models for analysis of music
dc.title.alternative en_US
dc.type Journal Article
dc.description.version Version of Record en_US
duke.date.pubdate 2010-6-0 en_US
duke.description.endpage 472 en_US
duke.description.issue 490 en_US
duke.description.startpage 458 en_US
duke.description.volume 105 en_US
dc.relation.journal Journal of the American Statistical Association en_US
pubs.issue 490
pubs.organisational-group /Duke
pubs.organisational-group /Duke/Institutes and Provost's Academic Units
pubs.organisational-group /Duke/Institutes and Provost's Academic Units/University Institutes and Centers
pubs.organisational-group /Duke/Institutes and Provost's Academic Units/University Institutes and Centers/Duke Institute for Brain Sciences
pubs.organisational-group /Duke/Pratt School of Engineering
pubs.organisational-group /Duke/Pratt School of Engineering/Electrical and Computer Engineering
pubs.organisational-group /Duke/Trinity College of Arts & Sciences
pubs.organisational-group /Duke/Trinity College of Arts & Sciences/Mathematics
pubs.organisational-group /Duke/Trinity College of Arts & Sciences/Music
pubs.organisational-group /Duke/Trinity College of Arts & Sciences/Statistical Science
pubs.publication-status Published
pubs.volume 105

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