REPORT paiement:rr08-33/IDIAP A Distance Model for Rhythms Paiement, Jean-François Grandvalet, Yves Bengio, Samy Eck, Douglas EXTERNAL http://publications.idiap.ch/attachments/reports/2008/paiement-idiap-rr-08-33.pdf PUBLIC Idiap-RR-33-2008 2008 IDIAP Published in J.-F. Paiement, Y. Grandvalet, S. Bengio, and D. Eck. A Distance Model for Rhythms. The 25th International Conference on Machine Learning (ICML 2008). 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.