Sampling the extrema from statistical models of music with variable neighbourhood search.

Abstract

Statistical models of music can be used for classification and prediction tasks as well as for generating music. There are several different techniques to generate music from a statistical model, but not all are able to effectively explore the higher probability extrema of the distribution of sequences. In this paper, the vertical viewpoints method is used to learn a Markov Model of abstract features from an existing corpus of music. This model is incorporated in the objective function of a variable neighbourhood search method. The resulting system is extensively tested and compared to two popular sampling algorithms such as Gibbs sampling and random walk. The variable neighbourhood search algorithm previously worked with predefined style rules from music theory. In this work it has been made more versatile by using automatically learned rules, while maintaining its efficiency