Online music teaching model based on machine learning and neural network

Abstract

Abstract In order to improve the efficiency of online music teaching and the recognition efficiency of teachers, students and various music symbols, this paper builds an online music teaching model based on machine learning and neural network algorithms, and proposes an improved orthogonal moment sub-pixel relocation algorithm. On the basis of pixel-level edge detection, the Zernike orthogonal moment sub-pixel relocation algorithm is used, and the corner point set of the edge is obtained through the CPDA corner detection algorithm. Then, the corner sub-pixel relocation is performed on these corner point sets separately, so as to make up for the problem of low positioning accuracy of the corner position of the edge by the common edge sub-pixel relocation algorithm. In addition, on the basis of image and video feature recognition, this article combines actual music teaching needs to construct an online music teaching model and conduct an experimental analysis on the performance of the model. The research results show that the model algorithm constructed in this paper is effective and can be applied to practice.