评估 AI 音乐创造力：基于价值、新颖性与惊喜的量化多维框架
Evaluating AI Musical Creativity: Towards a Quantitative Multi-Dimensional Framework Based on Value, Novelty, and Surprise
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arXiv 论文提出一个量化评估 AI 生成音乐创造力的框架，基于 Margaret Boden 的价值、新颖性与惊喜三维度理论，将其在音乐领域操作化为一组互补的量化指标。该框架可帮助研究者实证评估模型创意输出、指导模型开发并降低计算成本，也为未来生成式模型将这些指标作为学习目标、直接优化创造力奠定基础。
正文 · 原文
Abstract:Despite the increasing sophistication of generative Artificial Intelligence (AI), research on the evaluation of AI-generated musical creativity remains limited. To address this gap, this paper proposes a quantitative framework for the formalization and evaluation of AI musical creativity. Building on Margaret Boden's theory, which conceptualizes creativity in terms of value, novelty, and surprise, the framework adapts and operationalizes these three dimensions for the musical domain. On the hypothesis that certain aspects of musical creativity can be assessed with a degree of objectivity, a domain-specific set of quantitative metrics for evaluating the creativity of AI-generated music is introduced. Rather than reducing musical creativity to a single unifying measure, this article proposes an ensemble of complementary metrics that capture its different dimensions, recognizing the complexity of musical artifacts and the difficulty of defining meaningful multi-objective compositional criteria. The framework has two implications. First, it gives researchers and developers a principled methodology and quantitative tools for empirically evaluating creative outputs, enabling more informed model development and reducing unnecessary computational costs. Second, it lays the groundwork for future generative models to optimize directly for creativity by adopting these metrics as learning objectives.
| Subjects: | Sound (cs.SD); Artificial Intelligence (cs.AI); Computers and Society (cs.CY) |
| Cite as: | arXiv:2611.14634 [cs.SD] |
| (or arXiv:2611.14634v1 [cs.SD] for this version) | |
| https://doi.org/10.48550/arXiv.2611.14634 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Giorgio Franceschelli Dr. [view email]
[v1]
Tue, 10 Nov 2026 19:00:06 UTC (170 KB)
来源：arXiv cs.AI · arxiv.org