Deep Learning-Based Analysis of Emotional Expression and Performance Characteristics in Vocal Music Teaching

Nowadays, the rapid progress in art education and artificial intelligence technology has indirectly promoted the joint exploration of multimedia and computer technology. Vocal music is art with emotional expression, and performers should have a common emotional interaction with the emotional expression of music itself. Therefore, this paper proposes a data analysis of emotional expression and performance characteristics of vocal music teaching based on deep learning. First, Mel-Frequency Cepstral Coefficients (MFCC) and Gamma Tone Filter (GTF) features of audio are extracted. Then, two different features are sent into the same filter structure to extract emotional features. Finally, the audio fusion features output by the filter are input into the BiLTM network, and the high-dimensional emotion features are extracted based on the forward and reverse networks. Through the model training results, the analysis performance of the method proposed is relatively excellent, and the vocal music emotional features can be effectively extracted through a variety of fusion features. The performance of RMSE, RME and [Formula: see text] is the best when fusing MFCC and GIF features. When the audio features are relatively rich, this paper can get better weights to facilitate the iteration of the model, and use multi-modal feature fusion to effectively improve the prediction accuracy of Arousal and Valence values. It has important reference significance for the emotional analysis of multimodal vocal music and the emotional expression analysis of vocal music teaching.