REPORT paiement:rr07-70/IDIAP A Generative Model for Rhythms Paiement, Jean-François Grandvalet, Yves Bengio, Samy Eck, Douglas EXTERNAL http://publications.idiap.ch/attachments/reports/2007/paiement-idiap-rr-07-70.pdf PUBLIC Idiap-RR-70-2007 2007 IDIAP Published in Music, Brain, and Cognition workshop, NIPS 2007. Modeling music involves capturing long-term dependencies in time series, which has proved very difficult to achieve with traditional statistical methods. The same problem occurs when only considering rhythms. In this paper, we introduce a generative 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.