Evolving Musical Sequences with N-Gram Based Trainable Fitness Functions

Conventionally, automatic music composition is done by evolving music sequences whose fitness is evaluated by a human listener. This interactive approach has led to interesting results but is very time consuming. Here we propose a system that is capable of automatically generating music using an evolutionary algorithm (EA), replacing the human evaluation process with a trainable music evaluation algorithm. This algorithm can be trained on existing music samples, such as Mozart compositions for example. This kind of system could provide a fast and cheap music composition tool. The current evaluation system is implemented with an N-Gram language model. This paper discusses the system in two parts. Firstly, it describes the performance of the proposed music evaluation algorithm. Secondly, it discusses the impacts of different sequence-oriented genetic operators in the evolutionary algorithm. Part one of the experimental results show that the N-Gram model is able to distinguish the composer of piano compositions by Mozart, Beethoven and Chopin with up to 81.9% accuracy. Part two of the results show that some of the sequence-oriented operators increased the fitness of the generated melodies, but some operators did not. The impacts of these operators are discussed in the experimental results section. Significantly, the results also show that better classification accuracy does not necessarily lead to better evolved music, suggesting that perceptual relevance is also an important factor.