A Distance Model for Rhythms

Abstract

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.

Citation: A Distance Model for Rhythms, Jean-Francois Paiement, Yves Grandvalet, Samy Bengio, Douglas Eck, International Conference on Machine Learning (ICML), 2008.

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