Construction of Artificial Intelligence Music Teaching Application Model Using Deep Learning

Abstract

This paper investigates AI-based interactive music design and proposes a new music learning mode. It aids in the development of students’ inquiry skills and allows teachers to take the lead. At the same time, this paper systematically introduces the status of DL theory’s application in music teaching evaluation and uses DL theory to develop a mathematical model for an AI music teaching evaluation system. The construction method of an AI music teaching evaluation model based on DL is detailed in this paper. The model can assess the quality of AI music teaching after the network has been trained. The designed instructional quality evaluation NN is trained and measured in this paper to verify the model’s performance. The experimental results show that this model has a prediction accuracy of 94.79 percent, which is approximately 8.52 percent higher than the traditional methods. It has some practicality and feasibility, and it can serve as a useful benchmark for the development of various instructional quality evaluation systems.

Music Teaching Mode of Colleges and Universities Based On Hierarchically Gated Recurrent Neural Network (HGRNN) and Lyrebird Optimization Algorithm (LOA)

“…Chu [23] has created an interactive music design based on AI technology and presents a novel approach to music learning. This design not only enhances students' inquiry skills but also empowers teachers to guide the learning process.…”