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• W2111411013 abstract "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." @default.
• W2111411013 created "2016-06-24" @default.
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• W2111411013 date "2007-01-01" @default.
• W2111411013 modified "2023-09-27" @default.
• W2111411013 title "A Generative Model for Rhythms" @default.
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• W2111411013 hasPublicationYear "2007" @default.
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