A distance model for rhythms

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.

• Type: Conference
• Year: 2008
• Where: ICML
• Authors: Douglas Eck, Jean-François Paiement, Samy Bengio, Yves Grandvalet
• Keywords: Conditional Prediction Accuracy, Hidden Markov Model, ICML 2008, Machine Learning, Simple Rhythm Representation