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

<p>Psychological quality directly affects the playing level of a piano player’s ability. In this paper, we use the advantage of multi-source data information fusion to extract multiple physiological signal features of the piano player, combine with RBF kernel function to optimize the SVM emotion recognition, and form the DBN-SVM-based performance emotion state recognition algorithm. The initial feature set of physiological signals is established, the feature selection results for various classification tasks are examined, and the statistical data on effective features are obtained. The recognition of three emotional states of calmness, fear, and pleasure is done, and relevant strategies are proposed to cultivate good psychological qualities in players. The accuracy rate of emotional classification of the subjects is analyzed, and the accuracy rate of different subjects is more than 77.50%, indicating that the classification effect is evident. The psychological characteristics of the performers were clarified to facilitate the implementation of corresponding cultivation strategies.</p>
</abstract>

Psychological quality directly affects the playing level of a piano player’s ability. In this paper, we use the advantage of multi-source data information fusion to extract multiple physiological signal features of the piano player, combine with RBF kernel function to optimize the SVM emotion recognition, and form the DBN-SVM-based performance emotion state recognition algorithm. The initial feature set of physiological signals is established, the feature selection results for various classification tasks are examined, and the statistical data on effective features are obtained. The recognition of three emotional states of calmness, fear, and pleasure is done, and relevant strategies are proposed to cultivate good psychological qualities in players. The accuracy rate of emotional classification of the subjects is analyzed, and the accuracy rate of different subjects is more than 77.50%, indicating that the classification effect is evident. The psychological characteristics of the performers were clarified to facilitate the implementation of corresponding cultivation strategies.

Research on Strategies for Cultivating Piano Players’ Psychological Quality in the Information Age

Applied Mathematics and Nonlinear Sciences

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

Psychological quality directly affects the playing level of a piano player’s ability. In this paper, we use the advantage of multi-source data information...

Research on Strategies for Cultivating Piano Players’ Psychological Quality in the Information Age

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 21. März 2024

Akzeptiert: 17. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-1961

Schlüsselwörter
Multi-source data, Physiological signal features, GA algorithm, RBF kernel function, SVM emotion recognition, Mental quality cultivation

© 2024 Liuwanyue Shi, published by Sciendo

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

Research on Strategies for Cultivating Piano Players’ Psychological Quality in the Information Age

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 21. März 2024

Akzeptiert: 17. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-1961

SchlüsselwörterMulti-source data, Physiological signal features, GA algorithm, RBF kernel function, SVM emotion recognition, Mental quality cultivation

© 2024 Liuwanyue Shi, published by Sciendo

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

Schlüsselwörter
Multi-source data, Physiological signal features, GA algorithm, RBF kernel function, SVM emotion recognition, Mental quality cultivation