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

<p>The first part of this paper examines the aesthetic and application advantages of art design using human-computer interaction technology and develops a multimodal perceptual human-computer interaction system for art design. Multimodal data is obtained using multi-scale convolutional kernels for acoustic feature extraction and deep convolutional neural networks for multiple interaction image feature fusion. Finally, a test analysis is conducted to verify the system's effectiveness in this paper. According to the results, the system has an average wake-up success rate of 99.51% and a wake-up response time of 0.3665 seconds. Implementing human-computer interaction technology and deep neural networks in art design is effective and promotes the development of art design.</p>
</abstract>

The first part of this paper examines the aesthetic and application advantages of art design using human-computer interaction technology and develops a multimodal perceptual human-computer interaction system for art design. Multimodal data is obtained using multi-scale convolutional kernels for acoustic feature extraction and deep convolutional neural networks for multiple interaction image feature fusion. Finally, a test analysis is conducted to verify the system's effectiveness in this paper. According to the results, the system has an average wake-up success rate of 99.51% and a wake-up response time of 0.3665 seconds. Implementing human-computer interaction technology and deep neural networks in art design is effective and promotes the development of art design.

Deep neural networks for multimodal perception and human-computer interaction technology in art design

Applied Mathematics and Nonlinear Sciences

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

The first part of this paper examines the aesthetic and application advantages of art design using human-computer interaction technology and develops a...

Deep neural networks for multimodal perception and human-computer interaction technology in art design

Publié en ligne: 21 oct. 2023

Pages: -

Reçu: 25 janv. 2023

Accepté: 01 mai 2023

DOI: https://doi.org/10.2478/amns.2023.2.00702

Mots clés
Deep neural network, Multiscale convolutional kernel, Art design, Human-computer interaction system, Multimodal perception

© 2023 Yamin Zhang, published by Sciendo

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

Deep neural networks for multimodal perception and human-computer interaction technology in art design

Publié en ligne: 21 oct. 2023

Pages: -

Reçu: 25 janv. 2023

Accepté: 01 mai 2023

DOI: https://doi.org/10.2478/amns.2023.2.00702

Mots clésDeep neural network, Multiscale convolutional kernel, Art design, Human-computer interaction system, Multimodal perception

© 2023 Yamin Zhang, published by Sciendo

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

Mots clés
Deep neural network, Multiscale convolutional kernel, Art design, Human-computer interaction system, Multimodal perception