Toward Piano Teaching Evaluation Based on Neural Network

With the rise of piano teaching in recent years, many people participated in the team of learning steel playing. However, expensive piano teaching fees and its unique one-to-one teaching model have caused piano education resources to be very short, so learning piano performance has become a very extravagant event. The factors affecting music performance are varying, and there are many types of their evaluation such as rhythm, expressiveness, music, and style grasp. The computer is used to simulate this evaluation process to essentially identify the mathematical relationship between factors affecting music performance and evaluation indicators. The use of computer multimedia software for piano teaching has become a feasible way to alleviate the contradiction. This paper discusses the implementation method of piano teaching software, the issues of computer piano teaching, the computer teaching as one-way knowledge, and the lack of interaction. The neural network (NN) model is used to evaluate the piano performance and simulate teachers to guide students through their exercise. The performance of the proposed system is tested for the piano music of “Ode to Joy,” which is different from the collection of NN training samples, and is delivered ten times by another piano teacher, student A (piano level 6), and student B (piano level 5).

A Study of Piano Timbre Teaching in the Context of Artificial Intelligence Interaction

This paper provides an in-depth analysis and research on piano timbre teaching in the context of artificial intelligence interaction, a bold vision of piano teaching, proposes a feasible solution in terms of teaching modules in intelligent piano teaching for senior teachers, and proposes an implementation path for the integration of intelligent piano and piano teaching from the four main blocks of piano teaching. Based on the multiplicative harmonic model of monophonic signal, combined with the variability of timbre characteristics, an audio synthesis model with editable timbre is proposed, and the experimental results show that editing the timbre parameters in the model can realize timbre modification, and the synthesized timbre conforms to the piano timbre characteristics. Based on the timbre analysis and the timbre synthesis model, a piano timbre library generation system is designed. The detailed design of the software modules such as audio file reading and writing, audio information analysis, timbre parameter acquisition, timbre synthesis, and simulated performance is given. The system can generate piano timbre libraries of different qualities flexibly and meet the requirements of timbre realism. The teaching experiment designed for teaching practice from solo teaching, and the practice target is first-year undergraduate students in the university, and the practice period is six weeks, and finally, the feasibility of intelligent piano teaching application is analysed by combining the experimental results. Through the teaching objectives, teaching content, and teaching methods, teaching environment reflects intelligent piano teaching to make up for the limitations of traditional piano teaching. Analyse the development trend of intelligent piano teaching in the context of artificial intelligence interaction, and explore the value of intelligent piano teaching.

Music Online Education Reform and Wireless Network Optimization Using Artificial Intelligence Piano Teaching

The purpose is to realize the intelligent reform of piano online teaching and the intelligent optimization of wireless networks. Empirical research is realized with quantitative research and algorithm simulation as the starting point. First, regression fitting algorithm and Relief F weight algorithm are adopted to extract the effectiveness of each characteristic variable. Next, under the guidance of metric learning theory, K-Nearest Neighbors (KNN) in Projected Feature Space (P-KNN) algorithm is proposed to complete the hierarchical recognition of piano teaching influence features. Metric Learning With Support Vector Machine (ML-SVM) classification algorithm is employed to identify the feature performance affecting piano teaching. Finally, the performance of P-KNN algorithm and ML-SVM algorithm is compared with KNN algorithm and Information-Theoretic-Metric-Learning (ITML) algorithm. It is concluded that the recognition accuracies of P-KNN and ML-SVM are 82.78% and 83.97%, respectively. Based on the quantitative research on the characteristics affecting piano teaching, artificial intelligence and wireless network optimization are combined to explore the implementation path of intelligent technology in piano teaching reform, reflect the use value of modern science and technology in piano teaching, and innovate the process of music online education reform of piano teaching.

Penetration of Multimedia Technology in Piano Teaching and Performance Based on Complex Network

Multimedia network teaching, as a modern teaching mode, provides an opportunity for the establishment of new educational methods and creates conditions for the all-round expansion of educational functions. This article mainly studies the penetration analysis of complex network and multimedia technology to piano teaching and performance. This article introduces complex network and multimedia technology into piano teaching cases to classify, analyze, research and evaluate, remove the rough and save the essence, and screen out some typical teaching cases that meet modern learning theories, teaching requirements, and the characteristics of piano discipline. On this basis, carry forward the advantages, correct the disadvantages, and develop and design some network teaching cases for piano teachers to apply and research. Adopt a new teaching model to improve the original single-class teaching model that focuses on teacher lectures. The new teaching mode is supported by multimedia and network technology, especially multimedia technology, which makes piano teaching move towards personalized learning, and use the method of questionnaire survey to conduct questionnaire surveys on students and teachers to investigate the impact of multimedia piano teaching. Experimental data show that, in terms of multimedia attitudes, 29.50% of teachers believe that multimedia technology is very important in piano teaching and performance, 66.10% of teachers believe that multimedia technology is important in piano teaching and performance, and 4.40% of teachers believe that multimedia technology is in piano teaching and more important in performance. The experimental results show that multimedia and network technology have a good auxiliary effect on piano teaching and performance.

The Pygmalion Effect to Piano Teaching From the Perspective of Educational Psychology

Aiming at the problem of low student enthusiasm in piano teaching, this work tried to apply “Pygmalion effect” to piano teaching from the perspective of educational psychology. Forty-five students were chosen from nine classes in grades 2, 4, and 6 of a central elementary school in Beijing City using experimental research, and the experimental group and the control group were set up. A questionnaire was adopted to conduct the pre-test and post-test of the experiment, which were done to analyze the factors that affect the attitude of students toward music learning and the effect of piano teaching. The results show that the encouragement level of the piano teacher and the enthusiasm of the students in the piano course were significantly correlated at the 0.01 level. There was also a significant correlation between self-efficacy and student piano learning enthusiasm at the 0.01 level. Furthermore, the pre-test and post-test results showed that the students in the experimental group of each grade generally performed much better in piano learning than the students in the control group, which proves that the “Pygmalion effect” can play an excellent interventional role in piano teaching. In this research, the value of “Pygmalion effect” in the educational psychology of piano music education in primary schools was deeply studied to explore the possibility of a novel piano teaching model. The research results show that teacher encouragement can stimulate the subjective initiative of students and make them perform better in piano learning. This research provides reference and ideas for the combination of education- and psychology-related research in the music classroom.

Analysis of the Current Situation of Piano Education in Colleges and Universities in the Information Age and Research on Countermeasures

In view of the low application ability of piano improvisational accompaniment of music majors, this paper proposes a method of big data combined with MIDI keyboard and Kinect depth sensor to achieve the purpose of recognizing chord progression and judging fingering when students perform, and realizes the auxiliary teaching system. Firstly, the information of color and depth images is obtained, and the state transition diagram of chord transposition and chord gesture template library are constructed as the system initialization conditions. Secondly, using the traditional skin color modeling and background difference method as well as the current depth data, the gesture recognition is realized by template matching. Finally, the correctness of chord progression is judged, and comprehensive fingering application is used to score and evaluate. The experimental results show that the system has high robustness and can be effectively applied to piano teaching.

What Early User Involvement Could Look Like—Developing Technology Applications for Piano Teaching and Learning

Numerous technological solutions have been proposed to promote piano learning and teaching, but very few with market success. We are convinced that users’ needs should be the starting point for an effective and transdisciplinary development process of piano-related Tactile Internet with Human-in-the-Loop (TaHIL) applications. Thus, we propose to include end users in the initial stage of technology development. We gathered insights from adult piano teachers and students through an online survey and digital interviews. Three potential literature-based solutions have been visualized as scenarios to inspire participants throughout the interviews. Our main findings indicate that potential end users consider posture and body movements, teacher–student communication, and self-practice as crucial aspects of piano education. Further insights resulted in so-called acceptance requirements for each scenario, such as enabling meaningful communication in distance teaching, providing advanced data on a performer’s body movement for increased well-being, and improving students’ motivation for self-practice, all while allowing or even promoting artistic freedom of expression and having an assisting instead of judging character. By putting the users in the center of the fuzzy front end of technology development, we have gone a step further toward concretizing TaHIL applications that may contribute to the routines of piano teaching and learning.