A Distance Model for Rhythms

Jean-Francois Paiement
Yves Grandvalet
Samy Bengio
International Conference on Machine Learning (ICML) (2008)

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.

Research Areas