摘要： 音乐生成在人工智能时代取得了飞速发展，传统的音乐创作过程正逐渐被基于深度学习的生成模型所替代，尤其是近年来，生成对抗网络（GANs）、变分自编码器（VAEs）、Transformer架构、扩散模型及大语言模型等技术的应用，为音乐创作提供了全新的思路和方法。系统综述了人工智能在音乐生成中的最新研究进展，重点探讨了从离散符号表征到连续音频波形生成的技术演变路径，特别是如何在多模态生成、情感表达、创作控制等方面取得的突破。同时详细梳理了各类生成模型在娱乐与大众消费、专业音乐制作、音乐教育、音乐治疗与健康及游戏与交互媒体等多元场景中的实际应用，从生成质量、结构一致性、计算效率与用户控制性等维度评估了不同技术的优缺点及当前面临的挑战。最后，讨论了未来人工智能在音乐创作中的发展趋势，包括生成质量提升策略、人机协同创作模式、以及与音乐产业深度融合的潜在路径，为该领域的进一步研究提供参考。

Abstract: Music generation has witnessed rapid advancement in the age of artificial intelligence, with traditional music creation processes being gradually replaced by deep learning-based generative models. In recent years, in particular, the application of technologies such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Transformer architectures, diffusion models and large language models has offered entirely new ideas and approaches for music creation. This paper systematically reviews the latest research progress of artificial intelligence in music generation, focusing on the technological evolution from discrete symbolic representation to continuous audio waveform generation, and especially the breakthroughs achieved in multimodal generation, emotional expression, creative control and other aspects. Meanwhile, it elaborates on the practical applications of various generative models in diverse scenarios including entertainment and mass consumption, professional music production, music education, music therapy and health, as well as games and interactive media, and evaluates the advantages, disadvantages and current challenges of different technologies from the perspectives of generation quality, structural consistency, computational efficiency and user controllability. Finally, it discusses the future development trends of artificial intelligence in music creation, such as strategies for improving generation quality, human-machine collaborative creation modes and potential paths for in-depth integration with the music industry, thus providing a reference for further research in this field.