Intelligent music generation, one of the most popular subfields of computer
creativity, can lower the creative threshold for non-specialists and increase
the efficiency of music creation. In the last five years, the quality of
algorithm-based automatic music generation has increased significantly,
motivated by the use of modern generative algorithms to learn the patterns
implicit within a piece of music based on rule constraints or a musical corpus,
thus generating music samples in various styles. Some of the available
literature reviews lack a systematic benchmark of generative models and are
traditional and conservative in their perspective, resulting in a vision of the
future development of the field that is not deeply integrated with the current
rapid scientific progress. In this paper, we conduct a comprehensive survey and
analysis of recent intelligent music generation techniques,provide a critical
discussion, explicitly identify their respective characteristics, and present
them in a general table. We first introduce how music as a stream of
information is encoded and the relevant datasets, then compare different types
of generation algorithms, summarize their strengths and weaknesses, and discuss
existing methods for evaluation. Finally, the development of artificial
intelligence in composition is studied, especially by comparing the different
characteristics of music generation techniques in the East and West and
analyzing the development prospects in this field.

本文对智能音乐生成技术进行了综合调查和分析，提出了关键讨论，明确了各自的特点，并通过表格进行了总结。我们首先介绍了如何将音乐作为信息流进行编码和相关数据集，然后比较了不同类型的生成算法，总结了它们的优点和缺点，并讨论了现有的评估方法。最后，研究了人工智能在音乐创作方面的发展，特别是比较了东西方音乐生成技术的不同特点并分析了该领域的发展前景。