Multi-Objective Evolutionary Music For Algorithmic Composition Of Piano Phrases
Authors: Chan Jou Min and Jason Teo Tze Wi
Music composition is not an easy task. One needs to fully understand the music theory as well as the music instruments in order to compose a nice piece. Composers often meet a creativity bottleneck while composing. Thus, this research project uses a multi-objective approach to evolve automatically generated short piano phrases. The encoding for the algorithmic music composition is done using a Genetic Algorithm (GA). The initial music phrase is generated by randomly putting music notes, here known as genes to make up a complete chromosome. Music evaluation is often done by the human ear. However, by using suitable fitness functions, we may be able to get a more precise music evaluation. Since it is a multi-objective approach, a weighted-sum fitness is used. It is the combination of the Zipf’s Law slope and music intervals’ value (MI). In order to ensure the fitness function is working in the right way, user evaluations are varied out at the same time. A number of experiments are conducted to identify the different parameters that affect the final results of the evolved music phrase. Parameters such as number of generations and mutation rate are tuned. The findings show that the proposed approach was able to generate short piano phrases that in general were rated as satisfactory to good by a group of human evaluators.