Paiement, Jean-FrançoisGrandvalet, YvesBengio, SamyEck, Douglas2010-02-112010-02-112010-02-11200810.1145/1390156.1390249https://infoscience.epfl.ch/handle/20.500.14299/47027Modeling 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.text::conference output::conference proceedings::conference paper