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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.06277 |
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| _version_ | 1866918147010854912 |
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| author | Kim, Jinju Kim, Taehan Waheed, Abdul Hwan, Jong Singh, Rita |
| author_facet | Kim, Jinju Kim, Taehan Waheed, Abdul Hwan, Jong Singh, Rita |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_06277 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | No Encore: Unlearning as Opt-Out in Music Generation Kim, Jinju Kim, Taehan Waheed, Abdul Hwan, Jong Singh, Rita Computation and Language 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. |
| title | No Encore: Unlearning as Opt-Out in Music Generation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.06277 |