Construction and Application of Music Teaching Resources Based on Recurrent Neural Network

Abstract

Along with the rapid development of informational techniques, educational techniques are becoming increasingly important in the field of education, encouraging the reform and innovation of traditional educational concepts and teaching methods. Various music teaching aids have also become widely available. However, there is a scarcity of assistive software that is truly appropriate for music classroom instruction. A neural network is an artificial model that models and connects neurons, which are the basic units of the animal or human brain, to stimulate the nervous system’s learning, association, memory, and pattern recognition functions. We propose a recurrent neural network (RNN)-based music teaching resource construction, method, and application in this paper, focusing on the needs of music distance learning, analysing the user role needs of teachers, students, and system administrators involved in teaching and learning, and designing a mobile teaching platform in line with music teaching based on the characteristics of music teaching. The experimental results show that the system’s accuracy increases steadily with the number of iterations, and the accuracy fluctuation is stable, with an average accuracy of 72 percent and good system stability. As a result, research into this topic has significant implications for improving the quality and efficiency of music education in China, reforming the current school music education model, developing social music education, and realising music education for all and lifelong music education.