Dynamic nonparametric bayesian models for analysis of music
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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.
Published Version (Please cite this version)10.1198/jasa.2009.ap08497
Publication InfoRen, L; Dunson, D; Lindroth, S; & Carin, L (2010). Dynamic nonparametric bayesian models for analysis of music. Journal of the American Statistical Association, 105(490). pp. 458-472. 10.1198/jasa.2009.ap08497. Retrieved from https://hdl.handle.net/10161/4397.
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James L. Meriam Distinguished Professor of Electrical and Computer Engineering
Lawrence Carin earned the BS, MS, and PhD degrees in electrical engineering at the University of Maryland, College Park, in 1985, 1986, and 1989, respectively. In 1989 he joined the Electrical Engineering Department at Polytechnic University (Brooklyn) as an Assistant Professor, and became an Associate Professor there in 1994. In September 1995 he joined the Electrical and Computer Engineering (ECE) Department at Duke University, where he is now a Professor. He was ECE Department Chair from 2011
Arts and Sciences Distinguished Professor of Statistical Science
My research focuses on developing new tools for probabilistic learning from complex data - methods development is directly motivated by challenging applications in ecology/biodiversity, neuroscience, environmental health, criminal justice/fairness, and more. We seek to develop new modeling frameworks, algorithms and corresponding code that can be used routinely by scientists and decision makers. We are also interested in new inference framework and in studying theoretical properties
Professor of Music
I am a composer of instrumental and vocal music. My most recent works are T120, a piano trio composed for the Horszowski Trio (2021) and a Quintet for Soprano Saxophone and String Quartet (2019). My current project is a quartet for flute and three strings, commissioned by the Electric Earth Concert Series. My teaching centers on technical aspects of music, including classes on music theory, composition, and transcription. I also co-teach a cou
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