为音乐生成AI制定开源评估框架，推动透明发展
MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI
- 基于110位音乐信息检索专家反馈，构建13类开源评估标准
- 评测16个顶尖音乐生成模型，发布可公开参与的开源排行榜
- 适合关注AI伦理、音乐版权与技术透明性的研究者和开发者
自2023年以来，生成式AI在音乐领域迅速发展。尽管技术进步显著，音乐生成模型仍面临透明度不足、责任缺失等伦理挑战，存在复制艺术家作品的风险，凸显开放性的重要性。随着欧盟《人工智能法案》鼓励开放模型，众多生成模型被标为‘开放’，但其定义仍存争议。本文将近期提出的基于证据的大型语言模型开源评估框架适配至音乐领域，结合110名音乐信息检索（MIR）社区成员的反馈，优化为MusGO（Music-Generative Open AI）框架，包含8项必要和5项理想类别的开源标准。我们对16个前沿生成模型进行了评估，并发布一个完全公开、支持社区贡献的开源排行榜。本工作旨在厘清音乐生成AI中的开放性概念，推动其透明、负责任的发展。
原文摘要 · Abstract (English)
Since 2023, generative AI has rapidly advanced in the music domain. Despite significant technological advancements, music-generative models raise critical ethical challenges, including a lack of transparency and accountability, along with risks such as the replication of artists' works, which highlights the importance of fostering openness. With upcoming regulations such as the EU AI Act encouraging open models, many generative models are being released labelled as 'open'. However, the definition of an open model remains widely debated. In this article, we adapt a recently proposed evidence-based framework for assessing openness in LLMs to the music domain. Using feedback from a survey of 110 participants from the Music Information Retrieval (MIR) community, we refine the framework into MusGO (Music-Generative Open AI), which comprises 13 openness categories: 8 essential and 5 desirable. We evaluate 16 state-of-the-art generative models and provide an openness leaderboard that is fully open to public scrutiny and community contributions. Through this work, we aim to clarify the concept of openness in music-generative AI and promote its transparent and responsible development.
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