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

<p>In this paper, based on the mathematical description of chord tone groups, the gesture features extracted for the back of the hand, as well as the playing characteristics of different fingers, are different, and a neural network is used to extract the gesture features and information in the piano performance process. From the perspective of piano performance gesture features, combined with the two-way LSTM layer and attention mechanism layer in the neural network and the Softmax classification algorithm, we constructed a neural network model for piano performance evaluation based on digitalized elements and used the data analysis software to analyze the influence of the effect of piano performance based on digitalized elements in an example. The results show that the influence and dissemination evaluation value of the performance effect of the piano repertoire of “Canon” is the highest, with values of 0.85 and 0.712, respectively, and the influence and dissemination evaluation value of the performance effect of the piano repertoire of “To Alice” is the lowest, with the values of 0.06 and 0.05 respectively, which indicates to a certain extent that the digitalized elements have a facilitating role in the enhancement of the influence and dissemination of the piano performance. When in the ‘one’, ‘two’, ‘three’, and ‘X’ gesture states, the index, middle, ring, and little fingers have a high degree of bending, respectively. The index finger, middle finger, ring finger and little finger have high bending degrees, respectively, and the values are all greater than 73, which confirms that the characterization of playing gestures in piano performance based on digitized elements is of great significance in improving the influence of piano performance effect.</p>
</abstract>

In this paper, based on the mathematical description of chord tone groups, the gesture features extracted for the back of the hand, as well as the playing characteristics of different fingers, are different, and a neural network is used to extract the gesture features and information in the piano performance process. From the perspective of piano performance gesture features, combined with the two-way LSTM layer and attention mechanism layer in the neural network and the Softmax classification algorithm, we constructed a neural network model for piano performance evaluation based on digitalized elements and used the data analysis software to analyze the influence of the effect of piano performance based on digitalized elements in an example. The results show that the influence and dissemination evaluation value of the performance effect of the piano repertoire of “Canon” is the highest, with values of 0.85 and 0.712, respectively, and the influence and dissemination evaluation value of the performance effect of the piano repertoire of “To Alice” is the lowest, with the values of 0.06 and 0.05 respectively, which indicates to a certain extent that the digitalized elements have a facilitating role in the enhancement of the influence and dissemination of the piano performance. When in the ‘one’, ‘two’, ‘three’, and ‘X’ gesture states, the index, middle, ring, and little fingers have a high degree of bending, respectively. The index finger, middle finger, ring finger and little finger have high bending degrees, respectively, and the values are all greater than 73, which confirms that the characterization of playing gestures in piano performance based on digitized elements is of great significance in improving the influence of piano performance effect.

Analysis of the Influence of Digital Elements on the Effectiveness of Piano Performance

Applied Mathematics and Nonlinear Sciences

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

In this paper, based on the mathematical description of chord tone groups, the gesture features extracted for the back of the hand, as well as the playing...

Analysis of the Influence of Digital Elements on the Effectiveness of Piano Performance

Publicado en línea: 31 ene 2024

Páginas: -

Recibido: 15 dic 2023

Aceptado: 23 dic 2023

DOI: https://doi.org/10.2478/amns-2024-0130

Palabras clave
Neural network model, Softmax classification algorithm, Attention mechanism layer, Digitized elements, Piano performance effect

© 2024 Yibing Wang, published by Sciendo

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

Analysis of the Influence of Digital Elements on the Effectiveness of Piano Performance

Publicado en línea: 31 ene 2024

Páginas: -

Recibido: 15 dic 2023

Aceptado: 23 dic 2023

DOI: https://doi.org/10.2478/amns-2024-0130

Palabras claveNeural network model, Softmax classification algorithm, Attention mechanism layer, Digitized elements, Piano performance effect

© 2024 Yibing Wang, published by Sciendo

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

Palabras clave
Neural network model, Softmax classification algorithm, Attention mechanism layer, Digitized elements, Piano performance effect