The aesthetic emotional expression of piano music art in the background of Internet of things

Frontiers in Psychology

article

Xianhua Zhang¹  Qin Kang² 

[1] School of Music Education;College of Music, Hefei Normal University

关键词: Aesthetic Emotion Expression;    Piano Music Art;    musical emotion;    Internet of Things;    emotional expression;   

DOI  :  10.3389/fpsyg.2022.974586

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

With the continuous influx of artistic works, there are more and more studies on their emotional expression. How to judge whether musical works can bring joy, anger, sadness and joy to people? Is it joy over anger or anger over joy? Now in the era of the Internet of Things, the Internet of Things uses various information sensors, radio frequency identification technology, GPS, infrared sensors, laser scanners and other equipment and technologies to collect any objects and processes that need to be monitored, connected, and interacted in real time. By collecting various information such as sound, light, heat, electricity, mechanics, chemistry, biology, location and so on, and using various possible networks to connect, it can achieve intelligent perception, identification and management of objects and processes. The Internet of Things is an information carrier based on the Internet, traditional telecommunication networks and so on., so that all normal physical objects that can be individually located which can be connected together. Based on this, this paper mainly studies the aesthetic emotion expression analysis of piano music art in the context of the Internet of Things. It mainly talks about the classification of music characteristics, emotional theoretical models, and emotional induction methods. Finally, the experimental analysis of piano music and the use of brain wave technology are used to analyze the experimental data. The experimental results show that in the process of feature extraction and optimization, this paper optimizes the traditional feature extraction based on power spectral density through cognitive rules, and achieves the effect of feature dimension reduction on the basis of ensuring the recognition rate. This paper uses the topological properties of EEG to classify emotions. The results show that the emotion recognition rate under the four emotional states can reach 67.3%, which is much higher than the current highest level.

【 授权许可】

CC BY