Toward Piano Teaching Evaluation Based on Neural Network

Luo, Wanshu; Ning, Bin

doi:10.1155/2022/6328768

Cited by 10 publications

(6 citation statements)

References 12 publications

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“…Luo and Ning used a neural network model to evaluate piano performance and simulated teachers to guide students to practice. The abovementioned results are consistent with the results of this study, which all indicate that the use of deep learning techniques can improve the efficiency of piano performance evaluation [28].…”

Section: Analysis Of the Results Of The Piano Performancesupporting

confidence: 91%

Evaluation Strategy of the Piano Performance by the Deep Learning Long Short-Term Memory Network

Chang

Peng

2022

Wireless Communications and Mobile Computing

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With the development of society and the progress of technology, the piano education industry has a large market. In view of the problem of high payment fees in the piano education industry, the scientific and automatic nature of piano performance evaluation has attracted people’s attention. However, since most of the piano performance evaluation schemes are based on rules, the continuity of the piano music and the accuracy of playing are ignored. Therefore, the purpose is to design a scientific piano performance evaluation scheme that can play a certain role in the sustainable development of the piano education industry. Firstly, long short-term memory in deep learning is explored. Secondly, the musical characteristics of piano performance are analyzed according to the musical instrument digital interface. The piano music features are extracted, and a long short-term memory-based musical instrument digital interface piano performance evaluation model is constructed. Finally, it analyzes the number of hidden layers implemented in the long short-term memory model for piano performance evaluation. The accuracy of piano performance evaluation under different models is analyzed. Under the bidirectional long short-term memory network model, different piano performance levels are evaluated to realize the study of piano performance evaluation strategies. Compared with the accuracy of the recurrent neural network and the long short-term memory model with different hidden layers, the bidirectional long short-term memory model has the highest test accuracy, with an average of 69.78%. When the hidden layer of the bidirectional long short-term memory model is 3, the loss function L value is the smallest, which is 0.11. Different levels of piano skills are evaluated, and the results of the systematic evaluation are consistent with the performance of different levels. This shows that the BLSM model is feasible for the piano performance evaluation strategy system. This study not only conducts an in-depth analysis of the deep learning long short-term memory model but also proposes a long short-term memory-based musical instrument digital interface piano performance evaluation model. Additionally, the flaws such as the incomplete consideration of musical continuity and expressiveness when evaluating piano performance pieces have been compensated. Finally, through different model validations, the bidirectional long short-term memory model is concluded with good accuracy in piano performance evaluation. These conclusions provide theoretical research and practical significance for the accuracy of piano performance evaluation.

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Section: Analysis Of the Results Of The Piano Performancesupporting

confidence: 91%

Evaluation Strategy of the Piano Performance by the Deep Learning Long Short-Term Memory Network

Chang

Peng

2022

Wireless Communications and Mobile Computing

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“…ey used a neural network model to analyze piano performances as well as to imitate teachers guiding students through their activities. e presentation of the future system is analyzed using piano kinds of music from "Joy to ode," which varies from the neural network training samples and is played sometimes by a different piano instructor [9].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Evaluation System for Realizing the Ability of University Piano Teaching

2022

Mobile Information Systems

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Students benefit from teacher’s action during education in a variety of subjects. Identifying the need for a better understanding of the influence of various presentation types at different levels of student proficiency is extremely important. Moreover, these skills are also important to any technique of learning piano in a school environment. The significance of music in the school’s curriculum, new concepts for teaching music in the school, and new ideas for teaching piano were among them. This study focuses on a teaching environment and piano learning in which beginning and advanced piano students study to produce a particular style of staccato in three types of group instruction scenarios. The musical difficulty is only demonstrated through audio and observation of the teacher’s action performance. The comprehensive ability evaluation system for piano teaching can, to a certain extent, evaluate and sort the ability of college piano teaching. Therefore, this paper utilizes the attribute method in combination with instant analysis which provides seven attribute indicators. The approach is reasonable, and evaluating characteristics such as the level of the school’s teaching plan set and related psychological weights may well be revised as necessary to address users’ requirements. After completely integrating the qualities of the school and comprehensively examining various contributing factors, the students’ learning quality in different grades or the same grade of colleges and universities may be determined. A comparison against the existing schemes can effectively represent the school’s overall teaching level.

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“…The model may successfully assist piano novices in correcting mistakes and enhancing the precision and effectiveness of practicing. The paper [14] discussed the method for putting computerized piano instruction into practice, the problems with it, what imparts information in a one-way manner, and the absence of contact. There are many different methods for assessing music performance characteristics, including rhythm, expressiveness, music, and style understanding.…”

Section: Related Workmentioning

confidence: 99%

Exploring the Reform of Piano Course Teaching based on AGIL Model

Xueying Liu

2024

jes

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University music performance majors are strongly encouraged to take a piano fundamentals course as one of their necessary foundational topics. For college music achievement majors, it is very crucial to develop and raise students' piano playing proficiency and expertise. For the analysis and creation of complex systems, the AGIL model serves as a theoretical framework. This model may be used to create a complete methodology for piano instruction that emphasizes the growth of the student's flexibility, goal achievement, skill integration, and pause in action. Learning to play the piano may test these connections in the brain that control movement in students and people with poor motor abilities, and it can even improve coordination. Thus, we suggested the AGIL model for the reform of piano course teaching to further encourage the creative growth of piano instruction for music majors in colleges and universities in a diverse environment. The dataset used was made up of Chinese piano students. The performance measures used to assess suggested efficacy include ideal teaching, student performance, note-reading skill, teamwork ability, and student support rate. The evaluation's results indicate that the AGIL approach has provided 98.5% which seems to be useful for improving students' piano skills.

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Toward Piano Teaching Evaluation Based on Neural Network

Cited by 10 publications

References 12 publications

Evaluation Strategy of the Piano Performance by the Deep Learning Long Short-Term Memory Network

Evaluation Strategy of the Piano Performance by the Deep Learning Long Short-Term Memory Network

Comprehensive Evaluation System for Realizing the Ability of University Piano Teaching

Exploring the Reform of Piano Course Teaching based on AGIL Model

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