RESTORE gives music restorers more control over what AI removes or recreates

AI has been used to clean old recordings for a while now, so RESTORE is not claiming to have invented music restoration. Professional tools can already remove hiss and clicks, isolate vocals and instruments, repair damaged sections and rebuild missing audio, but this new research project is testing a more transparent way of doing several of those jobs together.

RESTORE is an experimental system developed by researchers Meiying Chen, Benjamin Thompson and Michael Heilemann. It is not a new consumer app people can download, and it is not currently being sold to studios or record labels.

What makes the project interesting is the amount of control it gives the person carrying out the restoration. Instead of feeding a damaged recording into AI and receiving one finished cleaned version, RESTORE separates the audio into different components that can be adjusted independently.

Those components include vocals, music, continuous background hiss, sudden noises and other remaining audio that does not fit neatly into one category. A restorer can then decide how much of each element should stay rather than allowing the AI to make every decision automatically.

That distinction matters because cleaning an old recording is not always as simple as removing everything that sounds imperfect. A crackle or hiss may come from damage, but aggressive restoration can also strip away details from a singer’s voice, an instrument or the atmosphere of the original recording.

RESTORE is designed to make those choices more interactive. The person using it can change the balance between the separated parts and hear the effect without committing immediately to one final version.

The system can also attempt to recreate some of the higher-frequency detail that may have disappeared from an old or damaged recording. Existing restoration technology can already rebuild missing audio in different ways, but RESTORE keeps its newly generated material separate from the surviving recording.

That means a restorer can clearly identify what the AI has created rather than having replacement audio quietly mixed into the original. They can then decide whether to use it, reduce it or leave it out completely.

This could become increasingly important as AI-generated audio gets harder to distinguish from genuine recordings. If part of an old performance has been lost forever, an AI system may be able to create something that sounds convincing, but that does not mean it has recovered the exact sound that was originally there.

The research is therefore as much about control and transparency as cleaning audio. RESTORE is testing whether AI can help with difficult restoration work while still showing the human operator which parts are original, which parts have been removed and which parts have been newly generated.

That approach could eventually interest record labels, broadcasters and archives holding large collections of historic recordings. Many of those recordings already benefit from digital restoration, but more flexible tools could make it easier to tackle damaged material without handing the final creative decision over to the software.

RESTORE remains a research project rather than a finished commercial product, but the idea behind it addresses a question that will become more important as AI audio improves. The challenge is no longer simply whether technology can make an old recording sound cleaner, but whether listeners can still tell what was genuinely preserved and what was recreated later.