Music Art Teaching Quality Evaluation System Based on Convolutional Neural Network

Abstract

With the rapid growth of music and art education in colleges and universities today, the development of associated teaching quality assessment (TQE) is still in its infancy. In truth, most modern music and art education has yet to build a rigorous and appropriate evaluation system based on actual classroom teaching quality. Simply adopting classroom TQE indicators and approaches from other disciplines would unavoidably lead to formalization of music TQE findings in some schools and institutions. It has no bearing on evaluation, feedback, or advancement. Therefore, this paper uses the superior performance of neural network to solve nonlinear problems and constructs a music art TQE method based on convolutional neural network (CNN). The completed work is as follows: (1) The basic situation of domestic and foreign research on music art TQE is introduced. Several commonly used TQE methods at home and abroad are analyzed, and the CNN evaluation method is comprehensively introduced. (2) The principle and network structure of CNN are expounded, and a TQE system conforming to music art is constructed. (3) The final experimental results reveal that the CNN model has higher accuracy and better performance than the BP neural network when using the trained CNN, TQE model to conduct tests.