深度学习在音乐生成中的研究与应用综述OA

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.