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A Distance Model for Rhythms
This video was recorded at 25th International Conference on Machine Learning (ICML), Helsinki 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.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
