A Distance Model for Rhythms (2008)
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BibTeX
@MISC{A08adistance,
author = {Jean-francois Paiement A and Yves Grandvalet A and Douglas Eck C and J. -f. Paiement and Y. Gr and S. Bengio and D. Eck and A Distance and Jean-francois Paiement and Yves Grandvalet and Samy Bengio and Douglas Eck},
title = {A Distance Model for Rhythms},
year = {2008}
}
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Abstract
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. 2 IDIAP–RR 08-33 1
