Survey on the Evaluation of Generative Models in Music
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866912595088244736 |
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| author | Lerch, Alexander Arthur, Claire Bryan-Kinns, Nick Ford, Corey Sun, Qianyi Vinay, Ashvala |
| author_facet | Lerch, Alexander Arthur, Claire Bryan-Kinns, Nick Ford, Corey Sun, Qianyi Vinay, Ashvala |
| contents | Research on generative systems in music has seen considerable attention and growth in recent years. A variety of attempts have been made to systematically evaluate such systems.
We present an interdisciplinary review of the common evaluation targets, methodologies, and metrics for the evaluation of both system output and model use, covering subjective and objective approaches, qualitative and quantitative approaches, as well as empirical and computational methods. We examine the benefits and limitations of these approaches from a musicological, an engineering, and an HCI perspective. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05104 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Survey on the Evaluation of Generative Models in Music Lerch, Alexander Arthur, Claire Bryan-Kinns, Nick Ford, Corey Sun, Qianyi Vinay, Ashvala Sound Artificial Intelligence Machine Learning Research on generative systems in music has seen considerable attention and growth in recent years. A variety of attempts have been made to systematically evaluate such systems. We present an interdisciplinary review of the common evaluation targets, methodologies, and metrics for the evaluation of both system output and model use, covering subjective and objective approaches, qualitative and quantitative approaches, as well as empirical and computational methods. We examine the benefits and limitations of these approaches from a musicological, an engineering, and an HCI perspective. |
| title | Survey on the Evaluation of Generative Models in Music |
| topic | Sound Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2506.05104 |