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Literature survey of multi-track music generation model based on generative confrontation network in intelligent composition

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成果类型:
期刊论文
作者:
Liu, Weiming
通讯作者:
Weiming Liu
作者机构:
[Liu, Weiming] Hunan City Univ, Coll Art, Yiyang 413000, Peoples R China.
通讯机构:
[Weiming Liu] C
College of Art, Hunan City University, Yiyang, China
语种:
英文
关键词:
Deep learning;Music;Deep learning;Development and applications;Intelligent composition;Literature survey;Model-based OPC;Multi tracks;Music generation;Network-based;Neural-networks;Verification results;Generative adversarial networks
期刊:
JOURNAL OF SUPERCOMPUTING
ISSN:
0920-8542
年:
2023
卷:
79
期:
6
页码:
6560-6582
基金类别:
This work was supported by the diversified reform of music and ear teaching in ordinary higher music majors (Grant no. 2017 Xiangcheng Institute Fa No.120.33).
机构署名:
本校为第一机构
院系归属:
艺术学院
摘要:
The production of traditional music is too complicated, consuming a lot of financial and human resources. Therefore, this paper aims to use artificial intelligence (AI) for songwriting and to explore the development and application of the Generative Adversarial Network (GAN) in smart music. An improved GAN-based Multi-Track Music (MTM)-GAN is established. The model is validated with the generation of 5 different music tracks for bass, drums, guitar, piano, and strings. The verification results are compared with the music generated by the existi...

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