Dynamic Nonparametric Bayesian Models for Analysis of Music

@inproceedings{Ren2008DynamicNB,
  title={Dynamic Nonparametric Bayesian Models for Analysis of Music},
  author={Lu Ren and David Dunson and Scott Lindroth and Lawrence Carin},
  year={2008}
}
The dynamic hierarchical Dirichlet process (dHDP) 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 dHDP 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… CONTINUE READING
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A Generative Model for Rhythms,

  • Paiement, J.-F, Y. Grandvalet, S. Bengio, D. Eck
  • in NIPS’2007 Music, Brain & Cognition Workshop,
  • 2007
Highly Influential
4 Excerpts

A Probabilistic Model of Melody Perception

  • X. Wei, J. Sun, X. Wang
  • Cognitive Science
  • 2008

Artificial Intelligence (UAI-2007)

  • F. Caron, M. Davy, A. Doucet, E. Duflos, P. Vanheeghe
  • 2008
1 Excerpt

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