Dirichlet process HMM mixture models with application to music analysis

Journal Article

A hidden Markov mixture model is developed using a Dirichlet process (DP) prior, to represent the statistics of sequential data for which a single hidden Markov model (HMM) may not be sufficient. The DP prior has an intrinsic clustering property that encourages parameter sharing, naturally revealing the proper number of mixture components. The evaluation of posterior distributions for all model parameters is achieved via a variational Bayes formulation. We focus on exploring music similarities as an important application, highlighting the effectiveness of the HMM mixture model. Experimental results are presented from classical music clips. © 2007 IEEE.

Full Text

Duke Authors

Cited Authors

  • Qi, Y; Paisley, JW; Carin, L

Published Date

  • August 6, 2007

Published In

Volume / Issue

  • 2 /

International Standard Serial Number (ISSN)

  • 1520-6149

Digital Object Identifier (DOI)

  • 10.1109/ICASSP.2007.366273

Citation Source

  • Scopus