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

Clicked: 212

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Weining WANG

https://orcid.org/0009-0006-0589-8157

Xiaofen XING

https://orcid.org/0000-0002-0016-9055

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

http://doi.org/10.1631/FITEE.2300359


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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publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300359"
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Abstract: 
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.

基于Transformer-GANs生成有风格调节的音乐

王伟凝,李嘉辉,李意繁,邢晓芬
华南理工大学电子与信息学院,中国广州市,510600
摘要:近年来,研究人员开发了各种算法来生成动听的音乐。然而,在生成过程中有时忽略了风格控制。音乐风格是指音乐作品呈现的具有代表性的特征,是音乐最突出的特质之一。本文提出一种创新的音乐生成算法,该算法能够根据指定的风格从零开始创作完整的音乐作品。算法引入了风格约束的线性生成器和风格鉴别器。风格约束生成器模拟MIDI事件序列,强调风格信息的作用。风格鉴别器应用对抗学习机制并引入两种创新的损失函数,以加强对音乐序列的建模。此外,本文首次建立了一个判别指标,以评估生成音乐与训练数据在音乐风格上的一致性。在现有公共数据集上,实验结果的客观和主观评价都表明我们的算法在音乐制作方面优于现有先进方法。

关键词:音乐生成;风格调节;Transformer;音乐情感;

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

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