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.


Presented at:
25th International Conference on Machine Learning (ICML)
Year:
2008
Note:
IDIAP-RR 08-33
Laboratories:




 Record created 2010-02-11, last modified 2018-09-13

n/a:
Download fulltextPDF
External links:
Download fulltextURL
Download fulltextRelated documents
Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)