An Order-Complexity Aesthetic Assessment Model for Aesthetic-aware Music
  Recommendation
Xin Jin Wu Zhou Jingyu Wang Duo Xu Yongsen Zheng
Computational aesthetic evaluation has made remarkable contribution to visual
art works, but its application to music is still rare. Currently, subjective
evaluation is still the most effective form of evaluating artistic works.
However, subjective evaluation of artistic works will consume a lot of human
and material resources. The popular AI generated content (AIGC) tasks nowadays
have flooded all industries, and music is no exception. While compared to music
produced by humans, AI generated music still sounds mechanical, monotonous, and
lacks aesthetic appeal. Due to the lack of music datasets with rating
annotations, we have to choose traditional aesthetic equations to objectively
measure the beauty of music. In order to improve the quality of AI music
generation and further guide computer music production, synthesis,
recommendation and other tasks, we use Birkhoff's aesthetic measure to design a
aesthetic model, objectively measuring the aesthetic beauty of music, and form
a recommendation list according to the aesthetic feeling of music. Experiments
show that our objective aesthetic model and recommendation method are
effective.