An Order-Complexity Model for Aesthetic Quality Assessment of Homophony Music Performance
This addresses the need for automated aesthetic evaluation to guide AI music generation and improve human performance, though it appears incremental in applying existing aesthetic measures to music.
The paper tackled the problem of objectively assessing aesthetic quality in homophony music performance, proposing a method based on Birkhoff's aesthetic measure that performs well in experiments.
Although computational aesthetics evaluation has made certain achievements in many fields, its research of music performance remains to be explored. At present, subjective evaluation is still a ultimate method of music aesthetics research, but it will consume a lot of human and material resources. In addition, the music performance generated by AI is still mechanical, monotonous and lacking in beauty. In order to guide the generation task of AI music performance, and to improve the performance effect of human performers, this paper uses Birkhoff's aesthetic measure to propose a method of objective measurement of beauty. The main contributions of this paper are as follows: Firstly, we put forward an objective aesthetic evaluation method to measure the music performance aesthetic; Secondly, we propose 10 basic music features and 4 aesthetic music features. Experiments show that our method performs well on performance assessment.