A Study of Computing Representation and Harmony Rules for Four-Voice Harmony Learning

Abstract

The goal of this study is to explicate a series of process relevant to the development of online-based harmony learning contents and to verify the validity of outputs by activating the automatic music composition algorithm based on artificial intelligence(AI) technology. Automatic music composition is a field that many researchers who study AI become interested in; and diverse attempts to adapt harmony to automatic music composition have been ceaselessly made and still ongoing. In this study, we took Monte Carlo Tree Search and had AI as an objective evaluator evaluate a learner’s solution using the rules and weights for the learning theory of harmony. In addition, we presented an exemplar to the various plausible solutions using artificial intelligence techniques that automatically generate music in accordance with the theory of harmony. The study has designed three types of voice-leading exercises in the four-voice setting: one fills out the lower three voices against a given soprano; one writes out the upper three against a given bass; and one provides the outer voices against given inner ones. The study has shown that the automatic music composition algorithm took appropriate recommendations from AI via MCTS and thus generated legitimate instances of harmony and voice leading and that scores assigned to the harmony rules successfully guide exemplars conforming to the rules. This study has limited the scope of harmony rules for beginners: the appropriate rules were singled out and defined; and then the algorithm was developed to enable automatic music composition according to these rules. This algorithm and learning methodology are expected to be of a great help in online harmony learning.