Application of Information Fusion Technology in Innovative Teaching of Music Theory Courses in Universities

Abstract

Abstract In this paper, we first investigate the steps to implement a fusion algorithm that determines the quality function through fuzzy theory. Then, we utilize D-S evidence theory for decision-level fusion to identify the target data based on the collected data. The two algorithms, fuzzy theory and D-S evidence theory, are then combined. The affiliation function in fuzzy theory calculates the information collected from the sensors to find out the confidence level and patterns. Finally, the information fusion technology was analyzed in terms of its usage rate in music theory teaching, its impact on piano playing, and its impact on music theory teaching. In terms of utilization rate, 21 student teachers had information fusion technology utilization rate between 50% and 60%, and 21 student teachers had information fusion technology utilization rate between 50% and 60%. In terms of the impact of information fusion technology on the teaching of music theory, a comparative analysis of the pre and post test data T=−6.55 (P<0.0001) showed that the difference in the post test data was significantly higher than that of the pre test. This indicates that the facilitating effect of information fusion technology on music theory teaching is obvious.