Composition And Evaluation Of Music Using Artificial Intelligence: A Genetic Algorithm Approach

Abstract

Abstract Advancement in technology has contributed enormously to music composition, distribution and consumption. Algorithmic music is the use of technologies and techniques to aid music composition. Currently, there are various algorithms including rule-based, memetic, deep convolutional generative adversarial networks (GAN), particle swarm optimization (PSO) and stochastic composition algorithm for composing music. However, music obtained from these algorithms may not be appealing although the algorithm used may produce the optimal music based on the criteria defined by the composer. Furthermore, the optimal music produced shows that the aesthetic quality of the music is limited. This study, therefore, seeks to compose monophonic music using genetic algorithm and assess how it is appealing to humans. Nine (9) melodies were composed. The compositions were three versions each for a GA terminated at ten thousand, twenty thousand and sixty thousand generations. The changes in total fitness scores over the various generations were plotted and analysed. It was concluded that the algorithm performed best when terminated at ten thousand generations by producing the optimal music around 750th generation. However, a survey conducted to evaluate the aesthetic performance of the algorithm through human evaluation demonstrated that the music obtained from the termination at 60000th generation was the most pleasant composition. This therefore suggests that although a GA algorithm may perform better in terms of composition time, it does not guarantee an aesthetic music.