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.