Other

Neutune

🇪🇺 KOREA, REPUBLIC OFRegistered 27/04 staff

Disclosed budget

€50 — €0

Meetings 12mo

Policy files

Accredited passes

Recent meetings

No recorded meetings with EU commissioners.

Mission & Goals

Neutune develops attribution infrastructure for AI-generated music. Our mission is to enable licensed and equitable use of musical content in AI applications, complementing opt-out protocols. We propose ISBC (International Standard Sound Block Code) as an identifier system for granular musical components, supported by rights-aware database architecture. Our intrinsic attribution approach embeds real-time usage tracking during AI generation, creating verifiable records for transparent compensation. We focus on establishing interoperable technical standards and machine-readable protocols that enable rightsholders to express conditional permissions beyond binary opt-out mechanisms. Our work addresses the critical gap between blocking AI usage entirely and enabling nuanced licensing frameworks with granular control. We advocate for attribution-aware infrastructure that allows rightsholders to track actual usage patterns and participate in AI music ecosystems with fair compensation mechanis

EU Legislative Interests

AI Act Implementation and GPAI Code of Practice (Measure 1.AI Act Implementation and GPAI Code of Practice (Measure 1.3 - Copyright Section): We are directly engaged in the Commission's consultation on machine-readable protocols for text and data mining rights reservations. Our contribution focuses on expanding the scope beyond binary opt-out mechanisms to include attribution-enabled conditional licensing frameworks that allow rightsholders to participate in AI ecosystems with transparent usage tracking and fair compensation. Digital Single Market Directive (Articles 3-4 on TDM exceptions): While the CDSM Directive establishes opt-out provisions for TDM, it does not provide infrastructure for rightsholders who choose to license their content. We advocate for complementary technical standards that enable licensed AI usage with granular attribution, addressing the gap between blocking mechanisms and viable licensing frameworks for AI-generated music. EU Copyright Framework Modernization: We propose the establishment of ISBC (International Standard Sound Block Code) as a component-level identifier system complementing existing standards (ISRC for recordings, ISWC for compositions). This addresses the technical challenge that AI music generation operates at granular levels (stems, loops, phrases) that current track-level identifiers cannot adequately capture, creating a metadata gap that prevents fair attribution and compensation. AI Music Attribution Standards and Interoperability: We have developed detailed technical specifications for metadata extensions enabling machine-readable expression of conditional permissions, inference-time usage tracking, and component-level attribution. These proposals demonstrate how attribution infrastructure can integrate with existing music industry standards rather than requiring parallel systems, facilitating practical adoption by labels, publishers, and collective management organizations. Platform Regulation and Transparency Requirements: Our work on real-time attribution logging and verifiable usage records directly supports transparency obligations under the AI Act. By embedding attribution into the generation process rather than applying it post-hoc, our approach provides the deterministic provenance tracking that regulators require for AI system accountability and rightsholder protection. Digital Services Act - Content Moderation and Rights Management: Attribution infrastructure enables platforms to implement granular content policies that go beyond binary takedown/allow decisions, supporting DSA objectives for transparent content governance while respecting intellectual property rights in AI-generated content contexts. Our policy engagement focuses on demonstrating that opt-out protocols and attribution infrastructure are complementary components of a complete AI and copyright compliance framework, not competing approaches. Both serve copyright protection goals: opt-outs enable rightsholders to block unauthorized usage, while attribution enables transparent licensed usage with fair compensation when rightsholders choose to participate.

Communication Activities

Submitted comprehensive response to EC consultation on TDM opt-out protocols under GPAI Code of Practice Measure 1.3, analyzing eight identified solutions and proposing attribution infrastructure as complementary framework. Previously submitted technical documentation to RIAA AI Attribution RFI demonstrating operational system capabilities. Published peer-reviewed whitepaper "From Generation to Attribution: Music AI Agent Architectures for the Post-Streaming Era" (NeurIPS 2025) detailing ISBC identifier system, BlockDB architecture, intrinsic attribution methodology, and DDEX extension proposals with XML specifications. Published companion whitepaper on ISBC standard demonstrating component-level identification for granular rights management. Developed detailed DDEX extension proposals: (1) New Resource Type "Block" for modular segments identified by ISBC, (2) New UseType "UseForGenerativeAIInference" for component usage during generation, (3) New BusinessModel "UsageBasedBlockSettlementModel" for per-use compensation. Complete XML implementations demonstrate integration with existing standards. Active participation in music industry conferences on panels ethics and contribution. Contributing expertise through WIPO AIII Technical Exchange Network, A2IM, and Music Biz memberships. Ongoing dialogue with collective management organizations, labels, and publishers on practical attribution infrastructure implementation. Operating MixAudio platform as public demonstration of attribution-aware AI generation at consumer scale, proving technical feasibility of real-time tracking, granular permissions, and automated compensation. Publishing thought leadership on Forbes, Music Ally, and industry platforms addressing binary opt-out limitations and inference-time attribution requirements. Committed to active participation in EC workshops following consultation, contributing operational experience to protocol development. Planning engagement with Parliament committees on AI Act implementation and copyright modernization. All communications emphasize attribution infrastructure complements opt-out mechanisms, supporting EU goals of protecting IP rights while enabling transparent AI innovation. Advocating that rightsholders need both blocking and licensing tools.