A Distance Model for Rhythms
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.
- URL: http://publications.idiap.ch/downloads/papers/2008/paiement-ICML-2008.pdf
- Related documents: http://publications.idiap.ch/index.php/publications/showcite/paiement:rr08-33
Record created on 2010-02-11, modified on 2016-08-08