Piano Performance Evaluation System Based on Neural Network and Its Application in Piano Teaching

This study proposes a piano performance evaluation system based on neural networks, aiming to help piano students accurately evaluate their piano performance level and further optimize the quality of piano teaching by utilizing machine learning methods. This system is based on neural network technology for modeling. Through signal processing and feature extraction of piano playing audio signals, the piano playing skills and music performance are evaluated separately, and a comprehensive evaluation result is finally obtained. At the same time, the system supports real-time performance evaluation and standardized evaluation, providing timely and accurate feedback and suggestions for students, and providing effective teaching aids for teachers. Experiments have shown that this system can accurately evaluate the level of piano students’ performance skills and music performance, and can provide effective teaching aids for students and teachers. Compared with traditional manual evaluation methods, this system has advantages such as accurate evaluation, fast feedback, and data analysis, which can bring more convenience and benefits to piano teaching. Therefore, the results of this study have practical significance and promotional value, and have broad application prospects in piano teaching.