The paper aims to address the challenge of unauthorized use of copyrighted music in generative music AI by proposing a framework that ensures creators receive fair compensation while maintaining transparency in data usage. Throughout our paper, we introduce methods that combine federated and split learning with privacy-preserving techniques such as digital watermarking, fingerprinting, and algorithmic similarity analysis to detect and track copyrighted material without exposing sensitive data. Their results demonstrate that integrating these detection techniques with a levy-based compensation model can significantly reduce potential litigation costs from a projected $350 million to around $22.5 million in a case study, paving the way for a more equitable ecosystem for both AI developers and music creators

This project tackles the issue of adapting copyright law for generative AI systems, and developing reliable and safe methods of detecting copyrighted music in datasets. We believe that our proposed framework could fundamentally change the way that copyright holders and AI developers work together, by introducing ethical business practices through integrating advanced watermarking and fingerprinting techniques into AI training processes. We ensure that artists receive fair compensation while safeguarding intellectual property rights. This approach not only fosters transparency in data usage but also creates a win-win scenario for both AI developers and music creators.

The rapid expansion of music AI technologies has led to the extensive use of large-scale datasets that often include copyrighted music without adequate oversight. Current legal and technical frameworks struggle to identify and quantify such copyrighted content, resulting in the under-compensation of copyright holders and potential violations of intellectual property rights. This study implements a unique approach to copyright detection. Utilizing federated learning (FL), our method trains models locally, preserving data privacy by keeping sensitive information on local servers while aggregating model updates centrally. Additionally, model fingerprinting assigns unique digital signatures to training data outputs, enabling precise tracking and verification of copyrighted material. Leveraging these techniques, our framework compiles a comprehensive catalog of artists and quantifies the number of songs present in the dataset, which is then integrated into our compensation mechanism to ensure fair remuneration for copyright holders. Our solution enhances transparency in data usage while delivering mutual benefits for both AI developers and creators, incentivizing a cooperative musical landscape where AI and creativity coexist.