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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)
-
Emmanuel Vincent
- INRIA Loria
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Details
- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Title of issue:
- 2nd IEEE Global Conference on Signal and Information Processing strony 89 - 98
- Language:
- English
- Publication year:
- 2014
- Bibliographic description:
- Raczyński S., Vincent E.: Genre-Based Music Language Modeling with Latent Hierarchical Pitman-Yor Process Allocation// 2nd IEEE Global Conference on Signal and Information Processing/ : , 2014, s.89-98
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/taslp.2014.2300344
- Verified by:
- Gdańsk University of Technology
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