A study on the comprehensive development of vocal education under the dual role of voice training and mental quality cultivation

Abstract

The development of modern vocal education presents a diversified trend, in which the application and development of artificial intelligence technology has a positive impact on voice training and mental quality training. In this paper, we first use the average amplitude difference function method to extract the fundamental tone of the music signal, and find that there is a misdetection of the fundamental tone in the process, which is smoothed by using the median smoothing algorithm and the linear smoothing algorithm mixed method, aiming at eliminating the interfering information in the music signal. On this basis, by improving the vocal feature matching of the DTW algorithm, the intelligent diagnosis of voice training in vocal music education is realized, with a view to improving the quality of students' voice training. According to the actual performance of students in the process of voice training, it is proposed to use the Bayesian Knowledge Tracking-based Music Resource Recommendation Model (BKT-ER) to help students master the knowledge points of vocal training, which prompts students to overcome the psychological pressure and bad emotions in playing, and helps to establish a good psychological quality. Finally, from the voice training and psychological quality training program, the integration of the two application program is developed, and an example analysis of the program is carried out. Under the dual role of voice training and psychological quality cultivation in vocal education, students' basic skills, emotional regulation ability, and psychological cognitive ability are effectively improved, and the mean value of its dimensions is greater than 3.5, which fully verifies the effectiveness of the fusion program in this paper.