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Predictive models for music
- DOI:
- 10.1080/09540090902733806
pages 253-272
Available online: 18 Nov 2010Abstract
Modelling 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 predictive models for melodies. We decompose melodic modelling into two subtasks. We first propose a rhythm model based on the distributions of distances between subsequences. Then, we define a generative model for melodies given chords and rhythms based on modelling sequences of Narmour features. The rhythm model consistently outperforms a standard hidden markov model (HMM) in terms of conditional prediction accuracy on two different music databases. Using a similar evaluation procedure, the proposed melodic model consistently outperforms an input/output HMM. Furthermore, these models are able to generate realistic melodies given appropriate musical contexts.
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- Citation information:
- Available online: 18 Nov 2010
Author affiliations
- a Idiap Research Institute, Centre du Parc, Rue Marconi 19, Case Postale 592, CH-1920, Martigny, Switzerland
- b Heudiasyc, CNRS/Université de Technologie de Compiègne, Centre de Royallieu, B.P. 20529, 60205, Compiègne, France
- c Google, 1600 Amphitheatre Pkwy, Mountain View, CA, 94043, USA
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