A Generative Model for Rhythms

Modeling music involves capturing long-term dependencies in time series, which has proved very difficult to achieve with traditional statistical methods. The same problem occurs when only considering rhythms. In this paper, we introduce a generative 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:
NIPS Workshop on Brain, Music and Cognition
Year:
2007
Note:
IDIAP-RR 07-70
Laboratories:




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

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