Research and Implementation of Automatic Composition System Based on ACMN

Abstract

The use of artificial neural network to compose music is a new computer composition method proposed in recent years. The advantage of this method is that it can process large-scale data in a relatively short time, greatly reducing the preparation work before computer composition and improving composition efficiency. However, there are still many problems that have not made obvious breakthroughs. For example, the yield is low, and a considerable proportion of the generated works have no appreciation value. They are completely random patchwork of notes and chords in the time dimension; the music produced is not pleasant, the melody is too simple and so on. In order to make the songs generated by the composition system more in line with the rules of music theory, this paper proposes a music generation model ACMN (Actor-Critic Music Network) based on the policy gradient method, which is characterized by the combination of reinforcement learning technology and neural network. Experiments show that, compared with the composition model without reinforcement learning technology, the ACMN model has more outstanding performance in terms of repetition rate and pleasantness.