Predictive models for music
Abstract
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.