Transparency First

Champion Artists

Safeguarding Creative Rights in the Age of AI

Sound Ethics is an independent research organization dedicated to serving artists, rights holders, and safeguarding creative rights in the age of AI.

Composers, songwriters, producers, musicians, and rights holders have been heavily disrupted by AI. We face serious challenges around copyright, fraud, deepfakes, and transparency when our songs are used to train and advance AI and machine learning models.

Grounded in university partnerships, our research advances responsible Gen-AI music systems.

Embrace AI. Champion Artists.

Sound Ethics

Absolute respect for creative copyright
Absolute respect for creative copyright
Standardizing AI Rights
Absolute respect for creative copyright
Standardizing AI Rights

Sound Ethics (Side A)

Transparency First

Champion Artists

Safeguarding Creative Rights in the Age of AI

Sound Ethics is an independent research organization dedicated to serving artists, rights holders, and safeguarding creative rights in the age of AI.

Composers, songwriters, producers, musicians, and rights holders have been heavily disrupted by AI. We face serious challenges around copyright, fraud, deepfakes, and transparency when our songs are used to train and advance AI and machine learning models.

Grounded in university partnerships, our research advances responsible Gen-AI music systems.

2026 University

Research

The Sound Ethics’ 2026 University Research Project is a large-scale, multi-institutional initiative, powered by an unprecedented and growing collaboration spanning 12 teams across 8 university DS, CS, and ML labs (including UC Berkeley, UCLA, NYU, UCI, UCSB) at the intersection of music & AI.

This is a critical moment: landmark litigation, rapidly evolving legislation, and the commercial expansion of generative AI are shaping precedents that may govern music and technology for decades.

Our Mission

Ethical AI in Academia

Sound Ethics

From Labs
To Legends

From
Labs to Legends
(
)

University DS, CS, and ML engineers are building the AI that will remake the music industry of tomorrow

Sound Ethics leads university teams and researchers working on AI / ML Music models, attribution, uncovering songs used in training models, and music intelligence that can identify when copyrighted material is used in AI-generated works.

In collaboration with universities, innovators, and industry leaders, we're establishing responsible AI research practices for the next generation of engineers that respect artists. We also provide opportunities for developing engineers who want to work in the music industry.

Standardizing AI Rights

Sound Ethics

IS YOUR
MUSIC

TRAINING AI?

We believe artists, songwriters, and rights holders deserve to know when their work trains AI. Our researchers study foundational AI music and audio machine learning models including which songs have been used in their development. Sound Ethics is carefully releasing its findings and preparing a public lookup tool that will make this history more visible and searchable.

What we do

RESEARCH

THE PROJECTS

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(01)

Training-Data Research

Uncovering the Songs

Our researchers examine foundational AI and machine learning models and match the songs and recordings inside them to the artists who made them.

(02)

Attribution

Interoperability & Infrastructure

We ask, "What does it take to interface with new attribution systems? What are the various approaches to attribution methods and how can they be benchmarked or evaluated by a neutral third party?"

(03)

DETECTION & PROVENANCE

Tracking Use in AI Outputs

We build and train models to identify AI-generated music, vocals, and speech, and uncover when copyrighted material is used in AI-generated works.

(01)

Training-Data Research

Uncovering the Songs

Our researchers examine foundational AI and machine learning models and match the songs and recordings inside them to the artists who made them.

(02)

Attribution

Interoperability & Infrastructure

We ask, "What does it take to interface with new attribution systems? What are the various approaches to attribution methods and how can they be benchmarked or evaluated by a neutral third party?"

(03)

DETECTION & PROVENANCE

Tracking Use in AI Outputs

We build and train models to identify AI-generated music, vocals, and speech, and uncover when copyrighted material is used in AI-generated works.

Latest Updates
Updates

Dive into the latest news, our documentation on standards, and helpful resources living at the intersection of AI and the music industry.

View all Updates
Sound Ethics Expands 2025 Research Collaborations
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Music & AI Rights Workshop
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