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Dynamic nonparametric bayesian models for analysis of music

dc.contributor.author Carin, Lawrence
dc.contributor.author Dunson, David B
dc.contributor.author Lindroth, S
dc.contributor.author Ren, L
dc.date.accessioned 2011-06-21T17:30:30Z
dc.date.issued 2010-06-01
dc.identifier.issn 0162-1459
dc.identifier.uri https://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.language.iso 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
dc.type Journal article
dc.description.version Version of Record
duke.date.pubdate 2010-6-0
duke.description.issue 490
duke.description.volume 105
dc.relation.journal Journal of the American Statistical Association
pubs.begin-page 458
pubs.end-page 472
pubs.issue 490
pubs.organisational-group Duke
pubs.organisational-group Duke Institute for Brain Sciences
pubs.organisational-group Electrical and Computer Engineering
pubs.organisational-group Institutes and Provost's Academic Units
pubs.organisational-group Mathematics
pubs.organisational-group Music
pubs.organisational-group Pratt School of Engineering
pubs.organisational-group Statistical Science
pubs.organisational-group Trinity College of Arts & Sciences
pubs.organisational-group University Institutes and Centers
pubs.publication-status Published
pubs.volume 105


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