<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The quality of vocal performance lies in the performer’s skill level, and in this paper, the optimization method of their objective functions is investigated by analyzing the relationship between generators and discriminators of generative adversarial networks. Meanwhile, the attention mechanism framework is introduced, and the attention mechanism is applied to the generator part of the generative adversarial network, which is used to focus significantly on the useful information of text features, thus establishing a GAN network based on the attention mechanism. Then, the importance of emotional expression in vocal performances is analyzed using the improved GAN model. In terms of the enhancement effect on the attractiveness of the performance, high levels of emotional expression increased on average by 24.78% compared to low levels. In terms of enhancing the audience’s aesthetic experience, the high level of emotional expression increased by an average of 25.41% compared to the low level. Regarding the promotion effect of the artistic imagination of the whole performance, the high level of emotional expression increased by 19.76% on average compared with the low level. The importance analysis based on GAN can provide a reference for performers to balance good singing skills and emotional expressions.</p>
</abstract>

The quality of vocal performance lies in the performer’s skill level, and in this paper, the optimization method of their objective functions is investigated by analyzing the relationship between generators and discriminators of generative adversarial networks. Meanwhile, the attention mechanism framework is introduced, and the attention mechanism is applied to the generator part of the generative adversarial network, which is used to focus significantly on the useful information of text features, thus establishing a GAN network based on the attention mechanism. Then, the importance of emotional expression in vocal performances is analyzed using the improved GAN model. In terms of the enhancement effect on the attractiveness of the performance, high levels of emotional expression increased on average by 24.78% compared to low levels. In terms of enhancing the audience’s aesthetic experience, the high level of emotional expression increased by an average of 25.41% compared to the low level. Regarding the promotion effect of the artistic imagination of the whole performance, the high level of emotional expression increased by 19.76% on average compared with the low level. The importance analysis based on GAN can provide a reference for performers to balance good singing skills and emotional expressions.

The Importance of Emotional Expression in Vocal Performance Art in the Internet Era

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The quality of vocal performance lies in the performer’s skill level, and in this paper, the optimization method of their objective functions is investigated...

The Importance of Emotional Expression in Vocal Performance Art in the Internet Era

Online veröffentlicht: 16. Sept. 2023

Seitenbereich: -

Eingereicht: 29. Nov. 2022

Akzeptiert: 27. März 2023

DOI: https://doi.org/10.2478/amns.2023.2.00338

Schlüsselwörter
Aesthetic experience, Emotional expression, Generative adversarial network, Attention mechanism, Text features

© 2023 Chao Zhang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

The Importance of Emotional Expression in Vocal Performance Art in the Internet Era

Online veröffentlicht: 16. Sept. 2023

Seitenbereich: -

Eingereicht: 29. Nov. 2022

Akzeptiert: 27. März 2023

DOI: https://doi.org/10.2478/amns.2023.2.00338

SchlüsselwörterAesthetic experience, Emotional expression, Generative adversarial network, Attention mechanism, Text features

© 2023 Chao Zhang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Aesthetic experience, Emotional expression, Generative adversarial network, Attention mechanism, Text features