Evolving cellular automata for music composition with trainable fitness functions

This thesis focused on the application of evolutionary computational techniques for music composition. Conventionally, the music evaluator in an evolutionary music composition system is either a human operating the system interactively, or a knowledge-based system. The objective of this study was to investigate a novel approach to music composition that combines a machine-learning based evaluator with a music generator. The evaluator is based on a machine-learning technique called N-gram modelling while Cellular Automata (CA) are used as the music generators. Hence Evolutionary Algorithms (EAs) are used to evolve CA capable of generating the style of music that the evaluators have been trained to rate highly. For the investigation of the N-gram model the experimental results showed that the discriminative power of the N-gram models were able to correctly classify composers with up to 80% accuracy in a composer classification task. An initial set of experiments used N-gram fitness functions to directly evolve musical sequences. The results showed that in order to evolve interesting music, appropriate musically meaningful genetic operators and constraints must be applied since optimal sequences (rated by the N-gram) tend to be extremely repetitive. However, some CAs show a natural ability for generating interesting music. The proposed CA-based evolutionary music composition system is able to evolve structured music without pre-defining a musical structure or a separate evolutionary process. Furthermore various types of fitness functions were proposed that aim to cooperate with N-gram fitness functions and evolve polyphonic music using a multi-objective evolutionary algorithm. Finally in order to evaluate the success of the proposed system and feedback, two online music surveys were conducted. The results showed that although on average the human-composed music was preferred to the evolved music, there is one piece of evolved music was close to indistinguishable from human-composed music.EThOS - Electronic Theses Online ServiceGBUnited Kingdo