提出音乐生成模型的删除机制，防止版权内容被无意使用。
No Encore: Unlearning as Opt-Out in Music Generation
- 将机器遗忘技术应用于文本转音乐模型，实现数据删除。
- 在移除训练数据后，模型性能下降可控，保持可用性。
- 为音乐生成模型的版权合规提供基础解决方案，适合开发者参考。
AI音乐生成正快速进入创意产业，支持通过文本描述生成音乐。然而，这类系统存在滥用受版权保护作品的风险，引发伦理与法律问题。本文首次探索将机器遗忘技术应用于预训练的文本到音乐（Text-to-Music, TTM）模型，分析现有遗忘方法在移除预训练数据时的有效性，同时维持模型性能。实验揭示了在音乐生成中应用遗忘技术的挑战，为未来在音乐生成模型中实现版权内容删除提供了基础分析。
原文摘要 · Abstract (English)
AI music generation is rapidly emerging in the creative industries, enabling intuitive music generation from textual descriptions. However, these systems pose risks in exploitation of copyrighted creations, raising ethical and legal concerns. In this paper, we present preliminary results on the first application of machine unlearning techniques from an ongoing research to prevent inadvertent usage of creative content. Particularly, we explore existing methods in machine unlearning to a pre-trained Text-to-Music (TTM) baseline and analyze their efficacy in unlearning pre-trained datasets without harming model performance. Through our experiments, we provide insights into the challenges of applying unlearning in music generation, offering a foundational analysis for future works on the application of unlearning for music generative models.
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