A Generative Model for Distance Patterns in Music

Abstract

In order to cope for the difficult problem of long term dependencies in sequential data in general, and in musical data in particular, a generative model for distance patterns especially designed for music is introduced. A specific implementation of the model when considering Hamming distances over rhythms is described. The proposed model consistently outperforms a standard Hidden Markov Model in terms of conditional prediction accuracy over two different music databases.

Citation: A Generative Model for Distance Patterns in Music, Jean-Francois Paiement, Yves Grandvalet, Samy Bengio, Douglas Eck, NIPS Workshop on Music, Brain and Cognition, 2007.

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