Dynamic nonparametric bayesian models for analysis of music
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2010-06-01
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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.
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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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Scott A. Lindroth
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.
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