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.