A Generative Model for Distance Patterns in Music
Venue
NIPS Workshop on Music, Brain and Cognition (2007)
Publication Year
2007
Authors
Jean-Francois Paiement, Yves Grandvalet, Samy Bengio, Douglas Eck
BibTeX
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.
