AIAPS

AIAPS (the AI Audio Protection Standard) is a personal project I built to give audio creators a way to formally declare their recordings off-limits for AI model training. It pairs a custom-built audio plugin with a Next.js and Supabase web platform, combining digital fingerprinting, inaudible watermarking, and a verifiable registry into a single standard.

The Plugin

The core of the project is a custom audio plugin distributed as a macOS standalone app, AU, and VST3, so creators can protect tracks directly inside their DAW. The plugin fingerprints the audio, embeds an inaudible AIAPS watermark, and registers the track in the AIAPS registry. All processing runs locally on the user's machine, so the audio itself never leaves their computer.

Protection & Verification

Each protected track carries a perceptual fingerprint designed to survive lossy compression like MP3 and AAC, alongside the embedded watermark. The platform exposes a verification flow so anyone can confirm a track's protected status through fingerprint lookup or watermark decoding, making the declaration independently checkable rather than self-asserted.

Web Platform

The web side is built on Next.js and Supabase, the same stack I used for Stagelet. Supabase handles authentication and stores the registry of protected works, with account login gating the dashboard where creators manage their registered tracks. The site also publishes the specification, compliance and governance documentation, verification tools, and an FAQ, currently in early access for macOS.

Why I Built It

As generative audio models advanced, I wanted a concrete, standards-style answer to the question of how creators can opt out of training. AIAPS was my attempt to design that end-to-end: the DSP and plugin tooling, the cryptographic fingerprint and watermark layer, the registry and verification infrastructure, and the public-facing spec, built as a self-directed project spanning native audio code and modern web development.