arXiv cs.AI· Hindy Rossignol, Giorgio Franceschelli, Mirco Musolesi·· 1 天前AI 评分24
基于价值、新颖性与惊喜度的 AI 音乐创造力量化多维评估框架
Evaluating AI Musical Creativity: Towards a Quantitative Multi-Dimensional Framework Based on Value, Novelty, and Surprise
AI 导读
研究者提出一套面向 AI 生成音乐的创造力量化评估框架，将 Margaret Boden 理论中的价值、新颖性与惊喜度三个维度适配到音乐领域并操作化。该框架不追求单一综合指标，而是给出一组互补的领域专用量化指标，用于实证评估生成音乐的创造性。其意义在于为研究者提供有原则的方法与工具，并让未来生成模型可将这些指标作为学习目标直接优化创造力。
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