PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Distance Model for Rhythms
Jean-Francois Paiement, Yves Grandvalet and Samy Bengio
In: ICML 2008(2008).


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.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:4731
Deposited By:Yves Grandvalet
Deposited On:24 March 2009