The Integration of Instrumental Elements and Vocal Music Teaching in Colleges and Universities Based on the Core Literacy View of Music

Abstract

Abstract In this paper, Fourier transforms in big data technology is used to realize the data preprocessing process of vocal signals from analog signals to digital signals and to explore the identification law of various music styles in vocal teaching in colleges and universities. According to the application of a convolutional neural network in vocal music teaching, the evaluation model of vocal music teaching based on a convolutional neural network is constructed by utilizing a convolutional neural network with accurate sensitivity and high-speed eigenvalue training speed. Starting from the design of relevant variables and the selection of evaluation tools, the experimental study of emotional therapy for the integration of percussion instruments and vocal music teaching is designed, and statistical analysis and simulation analysis are used to analyze the integration of musical instruments and vocal music teaching in the context of music core literacy. The results show that the evaluation error of the recurrent neural network method is 10.07%, while the minimum value is 0.24%. The maximum evaluation error of the convolutional neural network method is 8.22%, and the minimum evaluation error is 0.22%. The evaluation results of the quality of the integrated teaching of percussion instruments and vocal music of the model in this paper are better than the evaluation method of recurrent neural networks. During the maintenance period, when subject three was given weekly emotional therapy for teaching percussion instruments and vocal fusion, it was found that the subject’s emotional behavioral problems also completely subsided. This study realizes that the elimination of negative emotions in the teaching of musical instrument elements and college vocal music has a facilitating effect on the formation of students’ core musical literacy perspective.