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A Distance Model for Rhythms
| Content Provider | CiteSeerX |
|---|---|
| Author | Eck, Douglas Bengio, Samy Paiement, Jean-François Grandvalet, Yves |
| 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. 1. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Standard Hidden Markov Model Traditional Machine Music Data Specific Implementation Hamming Distance Distance Model Long-term Dependency Simple Rhythm Representation Different Music Database Conditional Prediction Accuracy |
| Content Type | Text |
