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.

翻译：随着ChatGPT的出现，公众对人工智能生成内容（AIGC）的认知开始重塑。人工智能显著降低了非专业人士在创意活动中的参与门槛，提升了内容创作的效率。近年来，符号化音乐生成的质量取得了显著提升，这得益于现代生成算法能够基于规则约束或音乐语料库提取音乐作品中隐含的模式。然而，现有文献综述往往对未来发展轨迹持传统且保守的观点，尤其缺乏对生成模型的全面基准测试。本文对近年来的智能音乐生成技术进行了综述与分析，阐述了各类技术的特性，并探讨了现有评估方法。此外，本文还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。