Music analysis with a Bayesian dynamic model

Published

Journal Article

A Bayesian dynamic model 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 model 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. Segmentation of a given musical piece is constituted via the model inference and the results are compared with other models and also to a conventional music-theoretic analysis. ©2009 IEEE.

Full Text

Duke Authors

Cited Authors

  • Ren, L; Dunson, DB; Lindroth, S; Carin, L

Published Date

  • September 23, 2009

Published In

Start / End Page

  • 1681 - 1684

International Standard Serial Number (ISSN)

  • 1520-6149

Digital Object Identifier (DOI)

  • 10.1109/ICASSP.2009.4959925

Citation Source

  • Scopus