TY - GEN
T1 - Dirichlet process HMM mixture models with application to music analysis
AU - Qi, Yuting
AU - Paisley, John William
AU - Carin, Lawrence
N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09
PY - 2007/8/6
Y1 - 2007/8/6
N2 - 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.
AB - 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.
UR - https://ieeexplore.ieee.org/document/4217446/
UR - http://www.scopus.com/inward/record.url?scp=34547502350&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2007.366273
DO - 10.1109/ICASSP.2007.366273
M3 - Conference contribution
SN - 1424407281
BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ER -