Welcome to Scholar Publishing Group

International Journal of Art Innovation and Development, 2023, 4(2); doi: 10.38007/IJAID.2023.040203.

Evaluation and Analysis of Psychological Stress in Vocal Performance of Ethnic Singers from the Perspective of Big Data Analysis Psychology

Author(s)

Boyan Yang and Huisuan Wei

Corresponding Author:
Boyan Yang
Affiliation(s)

Sichuan Conservatory of Music, Chengdu 610021, Sichuan, China

Abstract

In the contemporary music market, ethnic songs and music have become increasingly popular artistic expressions. In this form of performance, ethnic singers face certain challenges, including musical skills, emotional expression, and stage performance. Ethnic singers are prone to psychological tension during the performance process. Due to the potential for poor performance due to psychological tension, evaluating and quantitatively analyzing the psychological tension of ethnic singers in vocal performances can better understand and address these issues. This study aims to explore the psychological stress assessment analysis of ethnic singers' vocal performance from the perspectives of big data analysis and psychology. By collecting and analyzing physiological data (such as heart rate, breathing, etc.) and subjective feedback (such as anxiety level, stage confidence, etc.) during the vocal performance of ethnic singers, objective quantitative analysis of psychological tension can be obtained. Through experiments, it is known that the accuracy of the automatic analysis system for vocal performance status of ethnic singers can reach 95%. In addition, this article would also explore the impact of individual differences, performance backgrounds, and techniques among ethnic singers on psychological tension, as well as suggestions and guidance for adopting effective strategies to improve their performance and stage performance.

Keywords

Data Analysis, Psycho Analysis, Vocal Performance, Data Collection, Deep Learning

Cite This Paper

Boyan Yang and Huisuan Wei. Evaluation and Analysis of Psychological Stress in Vocal Performance of Ethnic Singers from the Perspective of Big Data Analysis Psychology. International Journal of Art Innovation and Development (2023), Vol. 4, Issue 2: 17-26. https://doi.org/10.38007/IJAID.2023.040203.

References

[1]Hariri R H, Fredericks E M, Bowers K M. Uncertainty in big data analytics: survey, opportunities, and challenges. Journal of Big Data, 2019, 6(1): 1-16.

[2]Wu Xinyu, Cao Nightjing. A brief analysis of the role of positive psychology in postgraduate career education . International Education Forum, 2022, 4(1):72-74.

[3]Zhang Zhengxi. Cultural Value and Inheritance Approach of Folk Song Art in the Yellow River Basin -- Review of The Study of Folk Song Art in the Yellow River Basin . Yellow River, 2022, 44(9):I0009-I0009.

[4]Guo Lilin. The Inheritance of Guangxi Folk Song Culture from the perspective of Intangible Culture Protection . Journal of Social Sciences of Jiamusi University, 2019, 37(3):162-164.

[5]Wei Han. Aesthetic Thinking on the Appreciation of Vocal Music Performance Art . Drama Home, 2019, 313(13):77-78.

[6]Liu B, Ding M, Shaham S, et al. When machine learning meets privacy: A survey and outlook. ACM Computing Surveys (CSUR), 2021, 54(2): 1-36.

[7]Yuji Roh, Geon Heo, Steven Euijong Whang. A survey on data collection for machine learning: a big data-ai integration perspective. IEEE Transactions on Knowledge and Data Engineering, 2019, 33(4): 1328-1347.

[8]Liu Ying and ZHANG Yuchun. Intervention Study of Receiving Music Therapy on Relieving Stage Tension of Music Performance . Art Science and Technology, 2021, 034(001):43-44.

[9]Lu Sicong. How to Overcome Stage Fright and Improve Confidence in Piano Performance . Northern Music, 2019, 39(7):191-192..

[10] Jiang H , Jiao R , Wu D , et al. Emotion Analysis: Bimodal Fusion of Facial Expressions and EEG. Computers, Materials and Continua, 2021, 68(2):2315-2327.

[11]Jia Yuanyuan. Analysis of data management and data analysis technology in Information technology era . Information Recording Materials, 2023, 24(2):82-84.

[12]Silva J . Stencil control in the automatic insertion of a PIM Company. International Journal for Innovation Education and Research, 2020, 8(10):417-440.

[13]Jin Yang,  Yuanjie Li,Qingqing Liu, et al. Brief introduction of medical database and data mining technology in big data era. Journal of Evidence‐Based Medicine, 2020, 13(1): 57-69.

[14]Wu Guangzhi. Application of adaptive resource allocation algorithm and communication network security in improving educational video transmission quality. Alexandria Engineering Journal, 2021, 60(5): 4231-4241.

[15]Zhang Junpeng Fan Yahui Lu Chenyi Deng Haoyi. Construction and application of the ability evaluation framework of inquiry experiment design in high school 

physics . Physical Experiment, 2022, 42(11):50-59.

[16]James Zou, Mikael Huss, Abubakar Abid, et al. "A primer on deep learning in genomics." Nature genetics 51.1 (2019): 12-18.

[17]Wang B , Zhang N , Lu W , et al. Intelligent Missing Shots' Reconstruction Using the Spatial Reciprocity of Green's Function Based on Deep Learning. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(3):1587-1597.

[18]Fu Shuanghui. The Promotion of Problem-based Learning to Deep Learning. Science and Technology Information, 2019, 17(5):192-193.

[19]Tan Xian-Dong PENG Hui. Ship target detection in SAR image based on Improved YOLOv5 . Computer Engineering and Applications, 2022, 58(4):247-254.

[20] Wagle S A , Harikrishnan R . A Deep Learning-Based Approach in Classification and Validation of Tomato Leaf Disease. Traitement du Signal, 2021, 38(3):699-709.