A Distance Model for Rhythms
| Type of publication: | Idiap-RR |
| Citation: | paiement:rr08-33 |
| Number: | Idiap-RR-33-2008 |
| Year: | 2008 |
| Institution: | IDIAP |
| Note: | Published in J.-F. Paiement, Y. Grandvalet, S. Bengio, and D. Eck. A Distance Model for Rhythms. The 25th International Conference on Machine Learning (ICML 2008). |
| Abstract: | 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. |
| Userfields: | ipdmembership={learning}, |
| Keywords: | |
| Projects: |
Idiap |
| Authors: | |
| Crossref by |
paiement:ICML:2008 |
| Added by: | [UNK] |
| Total mark: | 0 |
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