Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families
Proceedings of the 1st International Conference on Intelligent Systems for Molecular Biology
The Journal of Machine Learning Research
An empirical study of smoothing techniques for language modeling
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Topic modeling: beyond bag-of-words
ICML '06 Proceedings of the 23rd international conference on Machine learning
A hierarchical Bayesian language model based on Pitman-Yor processes
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
MySong: automatic accompaniment generation for vocal melodies
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A System for Automatic Chord Transcription from Audio Using Genre-Specific Hidden Markov Models
Adaptive Multimedial Retrieval: Retrieval, User, and Semantics
Stochastic Text Models for Music Categorization
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Genre classification using chords and stochastic language models
Connection Science - Music, Brain, Cognition
Evaluation methods for topic models
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Robust modeling of musical chord sequences using probabilistic N-grams
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Topic models with power-law using Pitman-Yor process
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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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.