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.
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.
Research Areas
