One ongoing concern about generative AI is whether copyrighted content has been used without authorization during training. In the music sector, where AI-created songs can strongly resemble the work of known artists, the issue has sparked both legal challenges and ethical questions.
Sony Group may have found a technological answer to the problem. Multiple reports indicate that the company has developed a system designed to identify copyrighted music embedded in AI-generated tracks. The tool can also assess how much each original work contributed to the final synthetic composition.
This attribution feature could prove to be an important development in the debate over generative AI. By identifying recognizable elements of original works within AI-generated content, the system may enable rights holders, both large labels and individual artists, to pursue compensation when their material contributes to a synthetic track.
The technology is said to rely on advanced neural fingerprinting and training-data attribution techniques. These approaches aim to track how generative AI systems learn from existing recordings, allowing analysts to detect influence even when the resulting track is not a direct sample or replica. Sony researchers have previously examined ways to identify which source files most strongly shaped a generated piece, laying the groundwork for clearer attribution.
The wider music industry hasn’t been standing still either. Sony Music and Universal Music Group have already worked with SoundPatrol to roll out neural fingerprinting tech that can spot traces of real, human-made songs inside AI-generated tracks.
If this new system works the way Sony hopes, it might take some of the heat off labels and streaming platforms. For the last couple of years, artists and music companies have been fighting AI tools they say were trained on copyrighted songs without permission. Streaming services haven’t had an easy time catching everything, and Sony by itself has tried to get tens of thousands of copycat AI tracks taken down.
In addition to enforcement, attribution technology could reshape the AI music landscape. By assessing the extent to which a generated track depends on particular source material, it may support licensing structures or revenue-sharing arrangements as alternatives to prohibiting AI content. This direction aligns with the industry’s emphasis on “ethical AI,” meaning systems trained on licensed material that provide compensation to creators.
The primary challenge moving forward is implementation. To be effective, the detection technology must function reliably across streaming platforms, extensive content libraries, and numerous AI generation services. It also needs to remain precise as AI models continue to evolve.
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