计算机工程与应用2026，Vol.62Issue(10)：1-25,25.DOI:10.3778/j.issn.1002-8331.2505-0135
深度学习在音乐生成中的研究与应用综述
Review on Research and Applications of Deep Learning in Music Generation
摘要
Abstract
Music generation is an important interdisciplinary research direction in artificial intelligence and computer science,which has achieved remarkable progress in recent years with the breakthroughs of deep learning technologies.This paper presents a systematic review of research and applications of deep learning in the field of music generation.Music representations are categorized into three types:symbolic representation,audio representation,and multimodal representation.The architectural characteristics and performance comparisons of mainstream deep generative models,including LSTM,VAE,GAN,Transformer,and diffusion models,are outlined.Music generation tasks are divided into three categories according to generation requirements:symbolic music generation,audio music generation,and vocal music generation,with representative methods and key technological advancements in each task category systematically sorted out.On this basis,commonly used datasets suitable for different generation tasks,as well as subjective and objec-tive evaluation metric systems,are summarized,and the performance of representative models is compared and analyzed through experimental data.The main challenges currently faced in music generation are discussed,including difficulties in music information modeling,data scarcity,lack of unified evaluation systems,and ethical and commercialization issues.Future application prospects in fields such as music education and psychotherapy are also explored.关键词
音乐生成/深度学习/生成式神经网络/智能作曲Key words
music generation/deep learning/generative neural networks/intelligent composing分类
信息技术与安全科学引用本文复制引用
赵明园,周若华,袁庆升,周祉彤..深度学习在音乐生成中的研究与应用综述[J].计算机工程与应用,2026,62(10):1-25,25.基金项目
国家自然科学基金(11590774). （11590774）
