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. 2022 Jul 21:2022:5135495.
doi: 10.1155/2022/5135495. eCollection 2022.

Comparative Analysis of Aesthetic Emotion of Dance Movement: A Deep Learning Based Approach

Ya Huang  1
Affiliations

Comparative Analysis of Aesthetic Emotion of Dance Movement: A Deep Learning Based Approach

Ya Huang. Comput Intell Neurosci. .

Retraction in

Abstract

Dance is a unique art with the human body movement as the main means, but dance is not limited to the human body movement itself. Like any art, dance is the product of human social behavior and a romantic behavior of human thoughts and emotions in the virtual world. Dances with different characteristics will also reflect different aesthetics, different cultural psychology, different living styles, and emotional trajectories of different times and different nationalities. People rely on the image of dance artists to develop and inherit the profound ideological connotation and philosophy of life. Viewers may form their own diversified and unique aesthetic characteristics. In the new era, in order to better promote the development, communication, and dissemination of dance art, it is very necessary to analyze and explore the connotation and aesthetic characteristics of dance art. Only through specific movements can the value and ideological connotation of works be expressed. Therefore, this paper comparatively analyzes dance movement aesthetic emotion based on deep learning. Experimentations are performed to systematically analyze the models from various perspectives. Findings of the evaluation show that CAP and CNN are effective models that can successfully extract high-level emotional features. The method proposes and effectively selects the best models among the five standard models based on key features and is, therefore, suitable in predicting the dancer's emotion and for the analysis of the dance movement in the future.

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Conflict of interest statement

The author declares that he has no conflicts of interest.

Figures

Figure 1
Figure 1
The main linguistic elements are covered by the art of dance.
Figure 2
Figure 2
Types of dance works.
Figure 3
Figure 3
The dance movement and the feeling structure.
Figure 4
Figure 4
Schematic diagram of the attention-based sequence-to-sequence model.
Figure 5
Figure 5
The network structure of the coding layer of the TOAC model.
Figure 6
Figure 6
Results of the comparative measurements.
Figure 7
Figure 7
The classification accuracy of each model on the dataset.
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