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

<p>Piano education has been paid more and more attention to in recent years. How to be able to teach in a more targeted way and improve the efficiency of students’ daily practice has become an urgent problem to be solved in the popularization of piano education. This paper innovatively combines artificial intelligence technology with piano education and designs an innovative education model for smart piano based on the cloud-based score, intelligent analysis, and gradient teaching functions of smart piano. The model utilizes the piano transcription algorithm of machine learning and the alignment technology of HMM to determine the learner’s piano practice and introduces the attention mechanism in the bidirectional recurrent neural network to realize the function of automatic piano harmony orchestration while testing the performance of both. On this basis, the designed intelligent piano education model is implemented, and the innovative effectiveness of the model is tested through comparative experiments. The results show that the completion of piano music and the artistic expression of music of students using the intelligent piano teaching mode improved by 10.097 points and 11.048 points, respectively, compared with students using the traditional teaching mode, and their p-values are less than 0.05, which is a significant improvement. This study enhances the variety of piano education and enhances piano learning efficiency.</p>
</abstract>

Piano education has been paid more and more attention to in recent years. How to be able to teach in a more targeted way and improve the efficiency of students’ daily practice has become an urgent problem to be solved in the popularization of piano education. This paper innovatively combines artificial intelligence technology with piano education and designs an innovative education model for smart piano based on the cloud-based score, intelligent analysis, and gradient teaching functions of smart piano. The model utilizes the piano transcription algorithm of machine learning and the alignment technology of HMM to determine the learner’s piano practice and introduces the attention mechanism in the bidirectional recurrent neural network to realize the function of automatic piano harmony orchestration while testing the performance of both. On this basis, the designed intelligent piano education model is implemented, and the innovative effectiveness of the model is tested through comparative experiments. The results show that the completion of piano music and the artistic expression of music of students using the intelligent piano teaching mode improved by 10.097 points and 11.048 points, respectively, compared with students using the traditional teaching mode, and their p-values are less than 0.05, which is a significant improvement. This study enhances the variety of piano education and enhances piano learning efficiency.

Research on Innovative Models of Piano Education Driven by Artificial Intelligence

Applied Mathematics and Nonlinear Sciences

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

Piano education has been paid more and more attention to in recent years. How to be able to teach in a more targeted way and improve the efficiency of...

Research on Innovative Models of Piano Education Driven by Artificial Intelligence

Data publikacji: 05 lip 2024

Zakres stron: -

Otrzymano: 18 mar 2024

Przyjęty: 06 cze 2024

DOI: https://doi.org/10.2478/amns-2024-1641

Słowa kluczowe
Piano transcription algorithm, HMM alignment technique, Recurrent neural network, Piano harmony arrangement, Intelligent piano education

© 2024 Chen Ji et al., published by Sciendo

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

Research on Innovative Models of Piano Education Driven by Artificial Intelligence

Data publikacji: 05 lip 2024

Zakres stron: -

Otrzymano: 18 mar 2024

Przyjęty: 06 cze 2024

DOI: https://doi.org/10.2478/amns-2024-1641

Słowa kluczowePiano transcription algorithm, HMM alignment technique, Recurrent neural network, Piano harmony arrangement, Intelligent piano education

© 2024 Chen Ji et al., published by Sciendo

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

Słowa kluczowe
Piano transcription algorithm, HMM alignment technique, Recurrent neural network, Piano harmony arrangement, Intelligent piano education