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Dirichlet process HMM mixture models with application to music analysis

Publication ,  Journal Article
Qi, Y; Paisley, JW; Carin, L
Published in: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
August 6, 2007

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.

Duke Scholars

Published In

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

DOI

ISSN

1520-6149

Publication Date

August 6, 2007

Volume

2
 

Citation

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Qi, Y., Paisley, J. W., & Carin, L. (2007). Dirichlet process HMM mixture models with application to music analysis. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2. https://doi.org/10.1109/ICASSP.2007.366273
Qi, Y., J. W. Paisley, and L. Carin. “Dirichlet process HMM mixture models with application to music analysis.” ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings 2 (August 6, 2007). https://doi.org/10.1109/ICASSP.2007.366273.
Qi Y, Paisley JW, Carin L. Dirichlet process HMM mixture models with application to music analysis. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2007 Aug 6;2.
Qi, Y., et al. “Dirichlet process HMM mixture models with application to music analysis.” ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2, Aug. 2007. Scopus, doi:10.1109/ICASSP.2007.366273.
Qi Y, Paisley JW, Carin L. Dirichlet process HMM mixture models with application to music analysis. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2007 Aug 6;2.

Published In

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

DOI

ISSN

1520-6149

Publication Date

August 6, 2007

Volume

2