Constructing a Music Network Teaching System by Using Neural Network Model with Wireless Audio Transmission

Abstract

The purpose is to solve the problems that the traditional teaching methods limit the openness and extension of the music classroom, the interaction between teachers and students, the environment of students' autonomous learning, and the music teaching situation. Wireless local area networks, Bluetooth, and intelligent transmission channels based on specific frequency can replace wired audio transmission and are widely used in the digital music classroom. Moodle system is used to build a music teaching network system based on the analysis of previous studies and the existing music teaching network platform. The system combines with Convolutional Neural Network (CNN) structure based on cloud computing to effectively identify and create music scores. The system effectiveness is further proved by analyzing the learning effect of the students and teaching effect of teachers in the conservatory of music. The results show that the system makes the experience of teachers and students in the teaching system different from before, and students can freely choose the time and place of class. In addition, the teaching method is flexible, and the teaching methods and resources are real-time. Therefore, in the music teaching network course, it overcomes some shortcomings of the traditional teaching mode. The music class is open and flexible. Teachers and students can have more interactive behavior and realize the students' self-study and music teaching environment. This exploration can provide a theoretical basis and practical experience for music-related teaching.