No Encore: Unlearning as Opt-Out in Music Generation

• URL: http://arxiv.org/abs/2509.06277v2
• Date: Wed, 24 Sep 2025 00:07:25 GMT
• Title: No Encore: Unlearning as Opt-Out in Music Generation
• Authors: Jinju Kim, Taehan Kim, Abdul Waheed, Jong Hwan, Rita Singh,
• Abstract summary: We present preliminary results on the first application of machine unlearning techniques to prevent inadvertent usage of creative content.<n>We analyze their efficacy in unlearning pre-trained datasets without harming model performance.
• Score: 21.703228676087367
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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