Optimization of College Music Teaching Mode Based on Embedded Neural Network

Abstract

The traditional teaching mode is difficult to fully meet the diversity of modern music education. This paper focuses on exploring new paths to optimize university music teaching models using advanced data algorithm technologies such as embedded neural networks. This exploration is not only an innovation of traditional teaching models, but also a key practice to promote students’ comprehensive development, enhance their overall quality, and stimulate innovative thinking. This paper deeply analyzes the urgency and importance of optimizing the music teaching mode in universities, and points out that in the rapidly changing digital age, music education must keep pace with the times to achieve a comprehensive upgrade of teaching content, methods, and evaluation system through technological means, in order to meet the diverse and high-quality demands of society for music talents. These technological advancements not only provide strong support for the integration of teaching resources and the design of personalized learning paths, but also open up vast space for the implementation of innovative teaching models. This paper introduces a new concept of spectral regression rationality. This concept aims to conduct in-depth analysis of music works through embedded neural networks to ensure the accuracy of music expression. At the same time, guide students to master scientific data analysis methods and cultivate their rational thinking and aesthetic abilities in music creation.