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)
Ren, L, D Dunson, S Lindroth and L Carin (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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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 of methods we develop.
Some highlight application areas:
(1) Modeling of biological communities and biodiversity - we are considering global data on fungi, insects, birds and animals including DNA sequences, images, audio, etc. Data contain large numbers of species unknown to science and we would like to learn about these new species, community network structure, and the impact of environmental change and climate.
(2) Brain connectomics - based on high resolution imaging data of the human brain, we are seeking to developing new statistical and machine learning models for relating brain networks to human traits and diseases.
(3) Environmental health & mixtures - we are building tools for relating chemical and other exposures (air pollution etc) to human health outcomes, accounting for spatial dependence in both exposures and disease. This includes an emphasis on infectious disease modeling, such as COVID-19.
Some statistical areas that play a prominent role in our methods development include models for low-dimensional structure in data (latent factors, clustering, geometric and manifold learning), flexible/nonparametric models (neural networks, Gaussian/spatial processes, other stochastic processes), Bayesian inference frameworks, efficient sampling and analytic approximation algorithms, and models for "object data" (trees, networks, images, spatial processes, etc).
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 course called "Music and the Brain" with Tobias Overath, a colleague in Psychology and Neuroscience. This class explores the physiology and psychology of hearing and music cognition, a study that has enabled me to reconsider ideas I know well from an entirely different perspective, with the result that "old" ideas have become "new" again. More marginal interests include some aspects of music technology: live sampling and signal processing, sonification, and computer-assisted composition.
Aside from these professional interests, I enjoy working with machine tools as well as motorcycling on back roads in North Carolina.
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 2011-2014, the Vice Provost for Research from 2014-2019, and since 2019 he has served as Duke's Vice President for Research. From 2003-2014 he held the William H. Younger Distinguished Professorship, and since 2018 he has held the James L. Meriam Distinguished Professorship. Dr. Carin's research focuses on machine learning (ML), artificial intelligence (AI) and applied statistics. He publishes widely in the main ML/AI conferences, and he has also engaged in translation of research to practice. He was co-founder of the small business Signal Innovations Group, which was acquired by BAE Systems in 2014, and in 2017 he co-founded the company Infinia ML. He is an IEEE Fellow.
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