A Generative Model for Rhythms

author: Jean-François Paiement, IDIAP Research Institute
published: Feb. 1, 2008,   recorded: December 2007,   views: 4198


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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.

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Reviews and comments:

Comment1 P, October 18, 2010 at 1:03 a.m.:

That euh was euh a euh very euh interesting euh talk euh...

Comment2 Roger M, March 20, 2011 at 11:17 p.m.:

Very interesting probabilitisc view of distances between segments in rhythms.

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