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