An Order-Complexity Model for Aesthetic Quality Assessment of Symbolic Homophony Music Scores
This work addresses the need for better quality assessment in AI-generated music, though it appears incremental as it builds on existing aesthetic measures.
The paper tackles the problem of poor aesthetic quality in AI-generated homophony music scores by proposing an objective quantitative evaluation method based on Birkhoff's aesthetic measure, resulting in a baseline model with eight basic and four aesthetic features.
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.