Paiement, Jean-FrançoisGrandvalet, YvesBengio, SamyEck, Douglas2010-02-112010-02-112010-02-112007https://infoscience.epfl.ch/handle/20.500.14299/47028Modeling 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.text::conference output::conference paper not in proceedings