ML Anthology

A Distance Model for Rhythms

Jean-François Paiement, Yves Grandvalet, Samy Bengio, Douglas Eck
ICML 2008 pp. 736-743
doi:10.1145/1390156.1390249 /icml/2008/paiement2008icml-distance/

Abstract

Modeling long-term dependencies in time series has proved very difficult to achieve with traditional machine learning methods. This problem occurs when considering music data. In this paper, we introduce a model for rhythms based on the distributions of distances between subsequences. A specific implementation of the model when considering Hamming distances over a simple rhythm representation is described. The proposed model consistently outperforms a standard Hidden Markov Model in terms of conditional prediction accuracy on two different music databases.

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Text

Paiement et al. "A Distance Model for Rhythms." International Conference on Machine Learning, 2008. doi:10.1145/1390156.1390249

Markdown

[Paiement et al. "A Distance Model for Rhythms." International Conference on Machine Learning, 2008.](https://mlanthology.org/icml/2008/paiement2008icml-distance/) doi:10.1145/1390156.1390249

BibTeX

@inproceedings{paiement2008icml-distance,
  title     = {{A Distance Model for Rhythms}},
  author    = {Paiement, Jean-François and Grandvalet, Yves and Bengio, Samy and Eck, Douglas},
  booktitle = {International Conference on Machine Learning},
  year      = {2008},
  pages     = {736-743},
  doi       = {10.1145/1390156.1390249},
  url       = {https://mlanthology.org/icml/2008/paiement2008icml-distance/}
}