An Order-Complexity Model for Aesthetic Quality Assessment of Homophony Music Performance
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 18 references that are in the map. First posted on arXiv 23 Apr 2023.
Builds on
5 of 18 references.
References · 5
- 2020MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessmentcited by 144
- 2016DeepBach: a Steerable Model for Bach Chorales Generationcited by 91
- 2018MIDI-VAE: Modeling Dynamics and Instrumentation of Music with Applications to Style Transfercited by 33
- 2018DeepJ: Style-Specific Music Generationcited by 12
- 2018Conditional End-to-End Audio Transformscited by 8
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Citing papers per year: 2026: 1.
1 paper.