TY - GEN
T1 - Music analysis with a Bayesian dynamic model
AU - Ren, Lu
AU - Dunson, David B.
AU - Lindroth, Scott
AU - Carin, Lawrence
N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09
PY - 2009/9/23
Y1 - 2009/9/23
N2 - 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.
AB - 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.
UR - http://ieeexplore.ieee.org/document/4959925/
UR - http://www.scopus.com/inward/record.url?scp=70349192867&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2009.4959925
DO - 10.1109/ICASSP.2009.4959925
M3 - Conference contribution
SN - 9781424423545
SP - 1681
EP - 1684
BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ER -