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Generative AI and Copyright Infringement: A Legal-Technical Analysis of AI Music Generation Systems Under 17 U.S.C. Title 17

arXiv 2606.26111 ↗First posted 20 May 2026cs.CY cs.AI cs.ETcited by 0

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Abstract (arXiv)

Generative artificial intelligence (GenAI) has enabled users to synthesize music with text prompts, combining copyrighted lyrics, AI-composed melodies, and synthetic vocals that imitate real artists. This paper examines the legal and technical dimensions of AI-based music creation (e.g., Google Gemini's music tools) under U.S. copyright law. We analyze whether a user who inputs one artist's protected lyrics into a GenAI system, directs it to use another artist's voice or style, publishes the resulting song, and monetizes it violates 17 U.S.C. Section 106's exclusive rights [3]. The analysis integrates Title 17 doctrine (rights of reproduction, derivative works, distribution), 17 U.S.C. Section 114's narrow sound recording protection [4], and the new voice-cloning laws emerging at the state level [20]. We argue that unauthorized lyric copying poses a high risk of infringement of the musical composition, whereas mere AI-generated voice imitation typically falls outside federal sound recording protection and instead implicates state publicity rights [12], [13]. Recent cases and legislation (Concord v. Anthropic [10]; Kadrey v. Meta [11]; Lehrman v. Lovo [12]; Tennessee's "ELVIS Act" [20]; UMG v. Uncharted Labs [14]; etc.) illustrate this split. We map AI technical components (prompt encoding, latent diffusion, neural vocoders, speaker embeddings) to legal risks and identify a regulatory gap: federal law robustly protects lyrics and melody but currently provides limited remedies for synthesized vocal likeness [22], [23]. The paper concludes with policy suggestions for clearer rules on AI music creation.

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  1. 2024Uncertain Boundaries: Multidisciplinary Approaches to Copyright Issues in Generative AI
  2. 2024Not All Similarities Are Created Equal: Leveraging Data-Driven Biases to Inform GenAI Copyright Disputes
  3. 2025Assessing the Effectiveness of Membership Inference on Generative Music
  4. 2025No Encore: Unlearning as Opt-Out in Music Generation
  5. 2025From Imitation to Innovation: The Emergence of AI Unique Artistic Styles and the Challenge of Copyright Protection
  6. 2025From Generation to Attribution: Music AI Agent Architectures for the Post-Streaming Era
  7. 2024Watermarking Training Data of Music Generation Models
  8. 2025Copyright Infringement Risk Reduction via Chain-of-Thought and Task Instruction Prompting