A Generative Model for Rhythms
| Type of publication: | Idiap-RR |
| Citation: | paiement:rr07-70 |
| Number: | Idiap-RR-70-2007 |
| Year: | 2007 |
| Institution: | IDIAP |
| Note: | Published in Music, Brain, and Cognition workshop, NIPS 2007. |
| 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. |
| Userfields: | ipdmembership={learning}, |
| Keywords: | |
| Projects: |
Idiap |
| Authors: | |
| Crossref by |
paiement:mbc:2007 |
| Added by: | [UNK] |
| Total mark: | 0 |
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