Classification of Styles and Expressive Characteristics in Higher Vocals Teaching - Based on Emotional Calculation Approach

Higher vocational vocal music teaching not only lies in enhancing students’ grasp of notes but also in deep emotional expression and emotional transmission. How to realize the precise classification of emotions expressed by musical style and features is an urgent problem to be solved. In this paper, starting from the classification of acoustic features and music styles, the STFT algorithm is used to extract the music features, and the music emotion model is constructed by combining the two-dimensional emotional psychological model. Based on the Turkish music emotion dataset, the acoustic data are preprocessed by using methods such as frame splitting, pre-emphasis, windowing, and mute frames, a deep confidence network extracts the music features, and the SVM model is introduced to establish the emotion classification model of music style and expression features. To verify the effectiveness of the model, this paper uses the dataset to analyze the music style and emotion classification dimensions, respectively. It is found that the classification accuracy of the Renaissance music category in classical music reaches 97.94%, and the accuracy of the music emotion classification of the model when the music data preprocessing slice granularity is 5 s is 0.838. The DBN-SVM model can realize the emotion classification of the music style and expression characteristics, which can provide a reference for the teachers to carry out targeted vocal music teaching and for the students to realize the release of emotion and emotional resonance when singing in vocal music. It provides a reference for teachers to carry out targeted vocal music teaching and for students to realize emotional release and resonance during vocal singing.