A Distance Model for Rhythms
Jean-Francois Paiement, Yves Grandvalet and Samy Bengio
In: ICML 2008(2008).
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.
|EPrint Type:||Conference or Workshop Item (Talk)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Subjects:||Theory & Algorithms|
|Deposited By:||Yves Grandvalet|
|Deposited On:||24 March 2009|