Music aesthetic teaching and emotional visualization under emotional teaching theory and deep learning

The study aims to overcome the shortcomings of the traditional music teaching system, for it cannot analyze the emotions of music works and does not have the advantages in music aesthetic teaching. First, the relevant theories of emotional teaching are expounded and the important roles of emotional teaching and aesthetic teaching in shaping students' personalities are described. Second, a music emotion classification model based on the deep neural network (DNN) is proposed, and it can accurately classify music emotions through model training. Finally, according to the emotional teaching theory and the model based on DNN, a visual system of music teaching is designed for visualizing the emotions, which is helpful to students' understanding of music works and the improvement of teaching effect. The results show that: (1) the teaching system designed has five parts, namely the audio input layer, emotion classification layer, virtual role perception layer, emotion expression layer, and output layer. The system can classify the emotions of the current input audio and map it to the virtual characters for emotional expression. Finally, the emotions are displayed to the students through the display screen layer to realize the visualization of the emotions of music works, so that the students can intuitively feel the emotional elements in the works. (2) The accuracy of the music emotion classification model based on DNN is more than 3.4% higher than other models and has better performance. The study provides important technical support for the upgrading of the teaching system and improving the quality of music aesthetic teaching.