Piano Music Teaching under the Background of Artificial Intelligence

Abstract

The work is aimed at solving the problems of easy trapping into local extremes and slow convergence speed of the traditional music teaching evaluation system on Backpropagation Neural Network (BPNN). The traditional note recognition methods are susceptible to high noise complexity. Firstly, the Levenberg Marquardt (LM) algorithm is used to optimize the BPNN; secondly, an improved endpoint detection algorithm is proposed by short-term energy difference, which can accurately identify the time value of each note in the piano playing audio. By the traditional frequency domain analysis method, a radical frequency extraction algorithm is proposed by the improved standard harmonic method, which can accurately identify the note’s pitch. Finally, a piano performance evaluation model by BPNN is implemented, and the model is implemented by the Musical Instrument Digital Interface (MIDI) system. This evaluation model can be used to correct the errors of students’ performances in the piano music teaching process and to perform overall evaluation, rhythm evaluation, and expressive evaluation. Teachers and students play minuet to collect experimental samples to train BPNN and test the performance of the evaluation model. The practical result shows that (1) after 3000 times of training, the neural network error is less than 0.01, and the network converges; (2) the evaluation results of the piano performance evaluation model designed are basically in line with the actual level of the performer and have specific feasibility; and (3) the optimized BPNN is used to correct errors during performances with an accuracy rate of 94.3%, which is 5.25% higher than the traditional method. The error correction accuracy rate for pitch is 92.9%, which is 5.21% higher than the traditional method. The optimized BPNN has significantly improved the error correction accuracy of the notes and pitches played by the player. The model can effectively help piano beginners correct errors and improve the accuracy and efficiency of the practice. The purpose of this study is to alleviate the scarcity of piano teachers, reduce the work intensity of piano teachers, realize automatic error correction and objective evaluation of playing, and provide necessary technical support for improving the efficiency of piano music teaching.

[Retracted] Innovative Practice of Multimusic Performance Culture Education of Core Literacy in Multimedia Environment

“…Huang et al put forward the application prospect of information technology in middle and university education. In order to maximize the role of multimedia technology in high school education [ 20 ], described a biological learning model based on multimedia technology and explained its ideas and implementation methods; Ke Heping (2002) proposed a multimedia teaching resource library and a network learning resource library and constructed several multimedia teaching resource libraries and network learning resource libraries; proposed the auxiliary role of multimedia technology in teaching at an early stage in China. She believes that the application of multimedia technology in teaching dilutes people's subjective consciousness and is too mechanized; in the study of through experimental research according to the analysis multimedia teaching needs to follow the characteristics of students' cognitive development, and the principles of multimedia teaching design need to be constantly tested and improved in teaching practice and adjusted according to the feedback effect of students; The teaching effect presented at the same time as auditory is better, so in multimedia teaching, visual and auditory representations should be used at the same time, not one-sided.…”

Exploring the Reform of Piano Course Teaching based on AGIL Model

“…They must skilfully use new media technologies and aggressively modify the educational and instructional approaches used in piano classes at colleges and institutions, To considerably boost students' music literacy and thoroughly develop their piano abilities, we must continually enrich and extend the teaching techniques used in college and university piano courses, as well as strengthen the teaching quality. The study [13] aimed to address the issues with the classic back-propagation neural network assessment method for music teaching's easy entrapment into local extremes and poor convergence speed. The classic note identification techniques are vulnerable to complicated noise.…”

Analysing The Development of Web-Based Multimedia Piano Playing Teaching Design Using Multiple Linear Regression Algorithm

“…The creation of an intelligent piano-playing training method is suggested in this study using an MLRA. The efficiency and accuracy of a proposed method are compared to those of ways such as support vector machines (SVM) [19], back propagation neural networks (BPNN) [20], and multiple signal classification (MSC) algorithms with artificial intelligence (AI) [21] to show the effectiveness of the method. These techniques are compared with previous techniques using several criteria, including accuracy, Latency, power, time, and mean square error.…”