New Thinking on Music Performance Teaching System Based on Artificial Intuition

Based on the existing research on AI music performance, this paper introduces the concept of artificial intuitive music performance from the professional perspective of music performance, so that AI music performance has the characteristics of humanization, and provides a promising research framework and path for researchers and learners. In this paper, some concepts, theories and research methods are introduced, the relational mapping-inversion principle of artificial intuitive music performance is proposed, and the learning mechanism of music perception and memory is described from the perspective of the performer's performance thinking and form. The research shows that through the hyper-cyclic learning process of artificial intuitive performance, an intuitive performance called performance attractor can be formed. At the same time, it is suggested that in the AI performance system, combining the characteristics of music performance theory, music type database, music structure database, and music formalization aesthetic database should be established. In addition, this artificial intuitive music performance system is also a music performance teaching system. Through the self-learning process of the system, it can simulate various styles of classical music and modern music, providing a new environment and platform for music learning and appreciation.