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

<p>The practical area of “piano performance” is one of the core areas in the piano classroom, and performance skills need to be applied in the practical area of “piano performance”. In this paper, the Leap Motion algorithm is used to extract the piano gesture movements, and the frame-by-frame processing is performed to encode the velocity direction of the fingers during the piano performance. Probabilistic predictions for forward and backward algorithmic sequences of fingers during playing are generated by using the HMM algorithm. To improve piano fingering automatic labeling, we propose combining the HMM algorithm with the judgment HMM algorithm and the Viterbi improvement algorithm for prioritizing piano fingering knowledge. The quantitative evaluation system for piano performance fingering is established to evaluate fingering in piano performance and check the performance scoring of the piano. The selection process included selecting three music clips that had a scoring rate above 0.8764, a gap between them that was not more than 0.06, and superior piano fingering generation quality. In the pre-post performance scoring of each dimension, the performer’s overall performance of the work on the overall appearance of the highest score, the highest score reached 84, the average score of 72.565, the piano performance of the follow-up effect is better. The assisted piano performance practice through digital technology helps piano players improve their performance skills and provides theoretical and practical references for the exploration of piano performance.</p>
</abstract>

The practical area of “piano performance” is one of the core areas in the piano classroom, and performance skills need to be applied in the practical area of “piano performance”. In this paper, the Leap Motion algorithm is used to extract the piano gesture movements, and the frame-by-frame processing is performed to encode the velocity direction of the fingers during the piano performance. Probabilistic predictions for forward and backward algorithmic sequences of fingers during playing are generated by using the HMM algorithm. To improve piano fingering automatic labeling, we propose combining the HMM algorithm with the judgment HMM algorithm and the Viterbi improvement algorithm for prioritizing piano fingering knowledge. The quantitative evaluation system for piano performance fingering is established to evaluate fingering in piano performance and check the performance scoring of the piano. The selection process included selecting three music clips that had a scoring rate above 0.8764, a gap between them that was not more than 0.06, and superior piano fingering generation quality. In the pre-post performance scoring of each dimension, the performer’s overall performance of the work on the overall appearance of the highest score, the highest score reached 84, the average score of 72.565, the piano performance of the follow-up effect is better. The assisted piano performance practice through digital technology helps piano players improve their performance skills and provides theoretical and practical references for the exploration of piano performance.

Exploring Dual Dimensions in Piano Performance Practice Based on Modern Digital Technology

Applied Mathematics and Nonlinear Sciences

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

The practical area of “piano performance” is one of the core areas in the piano classroom, and performance skills need to be applied in the practical area of...

Exploring Dual Dimensions in Piano Performance Practice Based on Modern Digital Technology

Pubblicato online: 31 gen 2024

Pagine: -

Ricevuto: 18 dic 2023

Accettato: 26 dic 2023

DOI: https://doi.org/10.2478/amns-2024-0137

Parole chiave
Leap Motion algorithm, Judgment HMM, Fingerings automatic labeling, Knowledge Viterbi improvement, Piano performance

© 2024 Yibing Wang, published by Sciendo

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

Exploring Dual Dimensions in Piano Performance Practice Based on Modern Digital Technology

Pubblicato online: 31 gen 2024

Pagine: -

Ricevuto: 18 dic 2023

Accettato: 26 dic 2023

DOI: https://doi.org/10.2478/amns-2024-0137

Parole chiaveLeap Motion algorithm, Judgment HMM, Fingerings automatic labeling, Knowledge Viterbi improvement, Piano performance

© 2024 Yibing Wang, published by Sciendo

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

Parole chiave
Leap Motion algorithm, Judgment HMM, Fingerings automatic labeling, Knowledge Viterbi improvement, Piano performance