A distance model for rhythms

Authors:
Jean-François Paiement;Yves Grandvalet;Samy Bengio;Douglas Eck
Affiliations:
IDIAP Research Institute, Martigny, Switzerland;IDIAP Research Institute, Martigny, Switzerland;Google, Mountain View, CA;Université de Montréal, Montréal, Québec, Canada
Venue:
Proceedings of the 25th international conference on Machine learning
Year:
2008

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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.