Genre-based music language modelling with latent hierarchical Pitman-Yor process allocation

Stanislaw Raczynski 1 Emmanuel Vincent 2
1 METISS - Speech and sound data modeling and processing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
2 PAROLE - Analysis, perception and recognition of speech
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : In this work we present a new Bayesian topic model: latent hierarchical Pitman-Yor process allocation (LHPYA), which uses hierarchical Pitman-Yor process priors for both word and topic distributions, and generalizes a few of the existing topic models, including the latent Dirichlet allocation (LDA), the bigram topic model and the hierarchical Pitman-Yor topic model. Using such priors allows for integration of n-grams with a topic model, while smoothing them with the state-of-the-art method. Our model is evaluated by measuring its perplexity on a dataset of musical genre and harmony annotations "3 Genre Database" (3GDB) and by measuring its ability to predict musical genre from chord sequences. In terms of perplexity, for a 262-chord dictionary we achieve a value of 2.74, compared to 18.05 for trigrams and 7.73 for a unigram topic model. In terms of genre prediction accuracy with 9 genres, the proposed approach performs about 33% better in relative terms than genre-dependent n-grams, achieving 60.4% of accuracy.
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Stanislaw Raczynski, Emmanuel Vincent. Genre-based music language modelling with latent hierarchical Pitman-Yor process allocation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, IEEE, 2014, 22 (3), pp.672-681. ⟨hal-00804567v2⟩

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