Machine Learning Applied to Harmonic Functions in Music Composition

Abstract

The knowledge representation process in Computer Music is an essential element for the development of systems. Methods have been applied to provide the computer with the ability to infer information from previously established experience and definitions. Alongside this process, with regard to the aspect of musical interaction, it is observed that in the generation of automatic musical compositions, the harmonic component is fundamental in the production of associations aimed at human interaction. However, there is a lack of studies that deal with the functions performed by chords. In this sense, Inductive Logic Programming is a growing field of research that incorporates concepts of Logic Programming and Machine Learning. This work consists in the application of the technique in Machine Learning of Inductive Logic Programming, performing the derivation of harmonic functions. Musical rules are represented by First-Order Logic and a corpus-based on Functional Harmony theory is used as background knowledge. Through the experimental method, the induction of 3 functional rules was performed. The measures of precision, coverage, accuracy, and execution time indicate the feasibility of this approach according to the purpose of algorithmic musical composition.