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

Stanisław Raczyński; Emmanuel Vincent

doi:10.1109/taslp.2014.2300344

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

Abstract

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, 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 3GenreDatabase (3GDB) andbymeasuringitsabilitytopredictmusicalgenrefromchord sequences. In terms of perplexit y, 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 -grams, ac hieving 60.4% of accuracy.

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Authors (2)

Stanisław Raczyński dr inż.
Emmanuel Vincent
- Inria, France Inria, France

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Category:: Articles
Type:: artykuł w czasopiśmie wyróżnionym w JCR
Published in:: IEEE Transactions on Audio Speech and Language Processing no. 22, pages 672 - 681,
ISSN: 1558-7916
Language:: English
Publication year:: 2014
Bibliographic description:: Raczyński S., Vincent E.: Genre-Based Music Language Modeling with Latent Hierarchical Pitman-Yor Process Allocation// IEEE Transactions on Audio Speech and Language Processing. -Vol. 22, nr. 3 (2014), s.672-681
DOI:: Digital Object Identifier (open in new tab) 10.1109/taslp.2014.2300344
Verified by:: Gdańsk University of Technology