An Order-Complexity Model for Aesthetic Quality Assessment of Symbolic Homophony Music Scores

TL;DR

This paper introduces an objective, quantitative model based on Birkhoff's aesthetic measure to evaluate the aesthetic quality of symbolic homophony music scores, addressing the gap in computational music aesthetics.

Contribution

It proposes a novel aesthetic evaluation model for homophony music scores and defines eight basic and four aesthetic music features for assessment.

Findings

Baseline model effectively evaluates music score quality.

Identified key features influencing aesthetic perception.

Provides a foundation for automated music aesthetics assessment.

Abstract

Computational aesthetics evaluation has made great achievements in the field of visual arts, but the research work on music still needs to be explored. Although the existing work of music generation is very substantial, the quality of music score generated by AI is relatively poor compared with that created by human composers. The music scores created by AI are usually monotonous and devoid of emotion. Based on Birkhoff's aesthetic measure, this paper proposes an objective quantitative evaluation method for homophony music score aesthetic quality assessment. The main contributions of our work are as follows: first, we put forward a homophony music score aesthetic model to objectively evaluate the quality of music score as a baseline model; second, we put forward eight basic music features and four music aesthetic features.