Year 2014

• Genre-Based Music Language Modeling with Latent Hierarchical Pitman-Yor Process Allocation

  Publication

  • S. Raczyński
  • E. Vincent

  - Year 2014

  In this work we present a new Bayesian topic model: latent hierarchical Pitman-Yor process allocation (LHPYA), which uses hierarchical Pitman-Yor pr ocess priors for both word and topic distributions, and generalizes a few of the existing topic models, including the latent Dirichlet allocation (LDA), the bi- gram topic model and the hierarchical Pitman-Yor topic model. Using such priors allows for integration of -grams with a topic...

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• Genre-Based Music Language Modeling with Latent Hierarchical Pitman-Yor Process Allocation

  Publication

  • S. Raczyński
  • E. Vincent

  - IEEE Transactions on Audio Speech and Language Processing - Year 2014

  In this work we present a new Bayesian topic model: latent hierarchical Pitman-Yor process allocation (LHPYA), which uses hierarchical Pitman-Yor pr ocess priors for both word and topic distributions, and generalizes a few of the existing topic models, including the latent Dirichlet allocation (LDA), the bi- gram topic model and the hierarchical Pitman-Yor topic model. Using such priors allows for integration of -grams with a topic model,...

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Year 2013

• Dynamic Bayesian Networks for Symbolic Polyphonic Pitch Modeling

  Publication

  • S. Raczyński
  • E. Vincent
  • S. Sagayama

  - IEEE Transactions on Audio Speech and Language Processing - Year 2013

  Symbolic pitch modeling is a way of incorporating knowledge about relations between pitches into the process of an- alyzing musical information or signals. In this paper, we propose a family of probabilistic symbolic polyphonic pitch models, which account for both the “horizontal” and the “vertical” pitch struc- ture. These models are formulated as linear or log-linear interpo- lations of up to fi ve sub-models, each of which is...

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• Melody Harmonization with Interpolated Probabilistic Models

  Publication

  • S. Raczyński
  • S. Fukayama
  • E. Vincent

  - Journal of New Music Research - Year 2013

  Most melody harmonization systems use the generative hidden Markov model (HMM), which model the relation between the hidden chords and the observed melody. Relations to other variables, such as the tonality or the metric structure, are handled by training multiple HMMs or are ignored. In this paper, we propose a discriminative means of combining multiple probabilistic models of various musical variables by means of model interpolation....

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