Predictive Models for Music
Venue
Connection Science, vol. 21 (2009), pp. 253-272
Publication Year
2009
Authors
Jean-Francois Paiement, Yves Grandvalet, Samy Bengio
BibTeX
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 predictive models for melodies. We
decompose melodic modeling 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 modeling sequences
of Narmour features. The rhythm model consistently outperforms a standard Hidden
Markov Model 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 Hidden Markov Model. Furthermore, these
models are able to generate realistic melodies given appropriate musical contexts.
