A Review of Intelligent Music Generation Systems

TL;DR

This paper surveys recent intelligent music generation techniques, analyzing their characteristics, evaluation methods, and regional differences, while discussing future development prospects in the field.

Contribution

It provides a comprehensive review and analysis of recent music generation methods, highlighting the lack of thorough benchmarking and comparing Eastern and Western approaches.

Findings

Modern generative algorithms improve symbolic music quality

Existing literature lacks comprehensive benchmarking of models

Regional differences influence music generation techniques

Abstract

With the introduction of ChatGPT, the public's perception of AI-generated content (AIGC) has begun to reshape. Artificial intelligence has significantly reduced the barrier to entry for non-professionals in creative endeavors, enhancing the efficiency of content creation. Recent advancements have seen significant improvements in the quality of symbolic music generation, which is enabled by the use of modern generative algorithms to extract patterns implicit in a piece of music based on rule constraints or a musical corpus. Nevertheless, existing literature reviews tend to present a conventional and conservative perspective on future development trajectories, with a notable absence of thorough benchmarking of generative models. This paper provides a survey and analysis of recent intelligent music generation techniques, outlining their respective characteristics and discussing existing methods for evaluation. Additionally, the paper compares the different characteristics of music generation techniques in the East and West as well as analysing the field's development prospects.