# No Encore: Unlearning as Opt-Out in Music Generation

arXiv: https://arxiv.org/abs/2509.06277 · HTML: https://mlsanity.com/paper/2509.06277/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Machine unlearning, one of 12 directions in LLM safety and security.
- Builds on 19 of its 34 references that are in the map.
- First posted on arXiv 8 Sep 2025.

## Placed in

- LLM safety and security / [Machine unlearning](https://mlsanity.com/safety/machine-unlearning/)

## Builds on (most cited first, 19 of 19)

- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks](https://mlsanity.com/paper/1911.04933/) (2019)
- [Erasing Concepts from Diffusion Models](https://mlsanity.com/paper/2303.07345/) (2023)
- [Simple and Controllable Music Generation](https://mlsanity.com/paper/2306.05284/) (2023)
- [SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation](https://mlsanity.com/paper/2310.12508/) (2023)
- [Unrolling SGD: Understanding Factors Influencing Machine Unlearning](https://mlsanity.com/paper/2109.13398/) (2021)
- [Efficient Training of Audio Transformers with Patchout](https://mlsanity.com/paper/2110.05069/) (2021)
- [Fr'echet Audio Distance: A Metric for Evaluating Music Enhancement Algorithms](https://mlsanity.com/paper/1812.08466/) (2018)
- [VALL-E 2: Neural Codec Language Models are Human Parity Zero-Shot Text to Speech Synthesizers](https://mlsanity.com/paper/2406.05370/) (2024)
- [Machine Unlearning in Generative AI: A Survey](https://mlsanity.com/paper/2407.20516/) (2024)
- [Dissecting Language Models: Machine Unlearning via Selective Pruning](https://mlsanity.com/paper/2403.01267/) (2024)
- [DiTTo-TTS: Diffusion Transformers for Scalable Text-to-Speech without Domain-Specific Factors](https://mlsanity.com/paper/2406.11427/) (2024)
- [Generative Unlearning for Any Identity](https://mlsanity.com/paper/2405.09879/) (2024)
- [MeLFusion: Synthesizing Music from Image and Language Cues using Diffusion Models](https://mlsanity.com/paper/2406.04673/) (2024)
- [Second-Order Information Matters: Revisiting Machine Unlearning for Large Language Models](https://mlsanity.com/paper/2403.10557/) (2024)
- [Presto! Distilling Steps and Layers for Accelerating Music Generation](https://mlsanity.com/paper/2410.05167/) (2024)
- [Video-Guided Text-to-Music Generation Using Public Domain Movie Collections](https://mlsanity.com/paper/2506.12573/) (2025)
- [Do Not Mimic My Voice: Speaker Identity Unlearning for Zero-Shot Text-to-Speech](https://mlsanity.com/paper/2507.20140/) (2025)
- [Hookpad Aria: A Copilot for Songwriters](https://mlsanity.com/paper/2502.08122/) (2025)

## Built on by (most cited first, 1 of 1)

- [Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks](https://mlsanity.com/paper/2607.07907/) (2026)

## Abstract (arXiv)

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.