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
2 PAROLE - Analysis, perception and recognition of speech

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.

Mots clés: Topic models; Hierarchical Pitman-Yor process; Chinese restaurant process; musical genre recognition; music information retrieval; chord model; genre model

Domaines: Machine Learning [stat.ML]

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, 2014, 22 (3), pp.672-681. ⟨10.1109/TASLP.2014.2300344⟩. ⟨hal-00804567v2⟩