A Distance Model for Rhythms

author: Jean-François Paiement, IDIAP Research Institute
published: Aug. 6, 2008,   recorded: July 2008,   views: 160
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Description

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.

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