Genre-based music language modelling with latent hierarchical Pitman-Yor process allocation
Identifieur interne : 000D02 ( Main/Exploration ); précédent : 000D01; suivant : 000D03Genre-based music language modelling with latent hierarchical Pitman-Yor process allocation
Auteurs : Stanislaw Raczynski [France] ; Emmanuel Vincent [France]Source :
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.
Url:
Affiliations:
- France
- Grand Est, Lorraine (région), Région Bretagne
- Metz, Nancy, Rennes
- Université de Lorraine, Université de Rennes 1, Université européenne de Bretagne
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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.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li><li>Région Bretagne</li></region><settlement><li>Metz</li><li>Nancy</li><li>Rennes</li></settlement><orgName><li>Université de Lorraine</li><li>Université de Rennes 1</li><li>Université européenne de Bretagne</li></orgName></list><tree><country name="France"><region name="Région Bretagne"><name sortKey="Raczynski, Stanislaw" sort="Raczynski, Stanislaw" uniqKey="Raczynski S" first="Stanislaw" last="Raczynski">Stanislaw Raczynski</name></region><name sortKey="Vincent, Emmanuel" sort="Vincent, Emmanuel" uniqKey="Vincent E" first="Emmanuel" last="Vincent">Emmanuel Vincent</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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