No Encore: Unlearning as Opt-Out in Music Generation

2025-09-07

AI Summary

This study addresses copyright infringement risks in AI music generation by pioneering the integration of machine unlearning into text-to-music (T2M) models, enabling creators to proactively “opt out” of copyrighted content present in pretraining data. Methodologically, we systematically evaluate multiple unlearning strategies on state-of-the-art pretrained T2M models, quantifying trade-offs between memory removal efficacy and generative fidelity. Experiments reveal substantial variation across methods in audio quality preservation, stylistic consistency, and suppression of copyrighted material—highlighting modality-specific challenges in music unlearning. We introduce the first dedicated evaluation framework for machine unlearning in music generation, featuring standardized metrics and benchmarks. This work establishes a reproducible methodological foundation and empirical evidence for copyright-compliant generative AI systems.

Technology Category

• Machine Learning: Unsupervised & Self-Supervised Learning
• Natural Language Processing: Generation
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Security and Privacy: Security and privacy of machine learning and AI applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Preventing inadvertent usage of copyrighted music in AI generation
• Applying machine unlearning techniques to text-to-music models
• Removing specific training data without degrading model performance

Innovation

• Applying machine unlearning to music generation models
• Opt-out copyrighted content without performance degradation
• Analyzing unlearning efficacy in text-to-music systems