Full Text:   <133>

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CLC number: TP39

On-line Access: 2024-02-19

Received: 2023-05-21

Revision Accepted: 2024-02-19

Crosschecked: 2023-10-29

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714


Weining WANG


Xiaofen XING


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Frontiers of Information Technology & Electronic Engineering  2024 Vol.25 No.1 P.106-120


Style-conditioned music generation with Transformer-GANs

Author(s):  Weining WANG, Jiahui LI, Yifan LI, Xiaofen XING

Affiliation(s):  School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510600, China

Corresponding email(s):   wnwang@scut.edu.cn, xfxing@scut.edu.cn

Key Words:  Music generation, Style-conditioned, Transformer, Music emotion

Weining WANG, Jiahui LI, Yifan LI, Xiaofen XING. Style-conditioned music generation with Transformer-GANs[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(1): 106-120.

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author="Weining WANG, Jiahui LI, Yifan LI, Xiaofen XING",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Style-conditioned music generation with Transformer-GANs
%A Weining WANG
%A Jiahui LI
%A Yifan LI
%A Xiaofen XING
%J Frontiers of Information Technology & Electronic Engineering
%V 25
%N 1
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2300359

T1 - Style-conditioned music generation with Transformer-GANs
A1 - Weining WANG
A1 - Jiahui LI
A1 - Yifan LI
A1 - Xiaofen XING
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 25
IS - 1
SP - 106
EP - 120
%@ 2095-9184
Y1 - 2024
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2300359

Recently, various algorithms have been developed for generating appealing music. However, the style control in the generation process has been somewhat overlooked. Music style refers to the representative and unique appearance presented by a musical work, and it is one of the most salient qualities of music. In this paper, we propose an innovative music generation algorithm capable of creating a complete musical composition from scratch based on a specified target style. A style-conditioned linear transformer and a style-conditioned patch discriminator are introduced in the model. The style-conditioned linear transformer models musical instrument digital interface (MIDI) event sequences and emphasizes the role of style information. Simultaneously, the style-conditioned patch discriminator applies an adversarial learning mechanism with two innovative loss functions to enhance the modeling of music sequences. Moreover, we establish a discriminative metric for the first time, enabling the evaluation of the generated music’s consistency concerning music styles. Both objective and subjective evaluations of our experimental results indicate that our method’s performance with regard to music production is better than the performances encountered in the case of music production with the use of state-of-the-art methods in available public datasets.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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