A Distance Model for Rhythms
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.
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.