Music generation with relation join

Abstract

Given a data set taken over a population, the question of how can we construct possible explanatory models for the interactions and dependencies in the population is a discovery question. Projection and Relation Join is a way of addressing this question in a non-deterministic context with mathematical relations. In this paper, we apply projection and relation join to music harmonic sequences to generate new sequences in a given composer or genre style. Instead of first learning the patterns, and then making replications as early music generation work did, we introduce a completely new data driven methodology to generate music. Then we discuss exploring the difference between the original music and synthetic music sequences using information theory based techniques.