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On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2024-05-24

Cited: 0

Clicked: 1100

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Kejun ZHANG

https://orcid.org/0000-0003-4592-1818

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Frontiers of Information Technology & Electronic Engineering  2024 Vol.25 No.7 P.1025-1030

http://doi.org/10.1631/FITEE.2400299


Suno: potential, prospects, and trends


Author(s):  Jiaxing YU, Songruoyao WU, Guanting LU, Zijin LI, Li ZHOU, Kejun ZHANG

Affiliation(s):  College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   yujx@zju.edu.cn, wsry@zju.edu.cn, 3210105631@zju.edu.cn, lzijin@ccom.edu.cn, zhouli@cug.edu.cn, zhangkejun@zju.edu.cn

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Abstract: 
Suno has attracted wide attention due to its impressive capabilities. It demonstrates technological advancements and opens up new possibilities for music composition, representing a milestone in the development of artificial intelligence (AI) music generation. In this paper, we first introduce the background and summarize the general technical framework of AI music generation, followed by an analysis of Suno’s advantages and disadvantages. Finally, we discuss the future trends in Music and AI.

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