DistroKid, TuneCore, CD Baby and others screen uploads for AI-generated audio. Check your AI score free and remove the artifacts so your release clears the gate the first time.
Distributors sit between you and the streaming platforms, and over the last two years they have quietly turned into a checkpoint for AI-generated audio. This is not about taste — it is about risk. When a distributor pushes a release to Spotify, Apple Music, YouTube Music, Amazon and dozens of smaller stores, it is vouching for that catalog. If a wave of low-effort, mass-produced AI tracks flows through, the distributor's relationship with those platforms is what gets damaged.
Three pressures are driving the screening. The first is rights and royalty fraud: bad actors have used generative tools to flood stores with thousands of near-identical tracks, farm fractional-cent streams with bots, and siphon royalties out of the pool that legitimate artists share. The second is platform pressure: the DSPs have started demanding that distributors filter obvious spam and disclose AI involvement, and they penalize partners whose catalogs are full of it. The third is simply moderation at scale — millions of uploads a week cannot be reviewed by humans, so the industry leans on automated detectors that score the audio itself.
The honest artist gets caught in the middle. If you used an AI tool to help write, arrange, generate a stem, or master, your track can score high on those detectors even though your creative intent is legitimate. The goal of this page is to help you understand the process, avoid borderline or false flags, and submit clean, correctly disclosed work — not to help anyone evade the rules.
It helps to know what the scanner is and is not doing. The AI check at a distributor is almost always an automated detector that analyzes the audio signal itself, not your metadata. It does not read your track title, your "written by" credits, or a checkbox that says "no AI here." It listens to the waveform and scores how likely it is to have been produced or processed by a generative model.
These detectors are trained to recognize the statistical fingerprint that generative and neural-processing tools leave behind. That fingerprint lives in places human ears mostly ignore:
Because the check scores the signal, two things follow. First, a purely instrumental or fully human track can occasionally score high (a false or borderline flag) — which is exactly why checking your own score before you submit is worth doing. Second, and more importantly, editing the tags or re-labeling the file does nothing to the audio, so it does nothing to the score. You can measure the same kind of score yourself with the free AI Checker before a distributor ever sees the file.
Being flagged is rarely a single, uniform event — it depends on the distributor and the confidence of the detector. In practice you can hit any of these outcomes:
None of that is catastrophic if you handle it honestly, but all of it costs time and momentum. The point of checking first is to replace an anxious guess with a number you can act on.
The most common mistake we see is bouncing the track again and hoping the score drops. It won't, and it helps to understand why. The AI fingerprint is baked into the content of the audio — the spectral, phase and temporal patterns described above. A fresh export, a format change from WAV to FLAC, a louder master, a new limiter, or a different sample rate all repackage the same underlying signal. The artifacts survive the bounce and survive the master.
Removing the flag means actually altering those artifacts in the audio, carefully, without destroying the musical content you care about. That is a targeted DSP problem, not an export setting — and it is the specific thing the AI Cleaner is built to do.
artefactFX gives you the same two tools a careful engineer would want: a measurement and a treatment. The workflow is deliberately simple.
Cleaning is honest signal processing. It reduces the artifacts; it cannot rewrite reality, and no tool can promise a 100% pass at every distributor. What it does is move a borderline or high-scoring track into far safer territory and give you the data to decide with confidence.
Before you hit submit at any distributor, run this short checklist. It takes minutes and saves release dates.
You can clean a track two ways, and which one fits depends on where the AI content lives. If your whole record was generated or heavily neural-processed, cleaning the full mix is the fast path — one file in, one clean pre-master out. If only one element is the problem — say an AI-generated vocal over an otherwise human production, or a single generated stem in an arrangement — treating that element on its own is more surgical and preserves the rest of the mix untouched. The stem route also gives you more control when you want to re-balance after cleaning. Start with a check on the full mix; if the score is being driven by one part, clean at the stem level.
The approach is distributor-agnostic — DistroKid, TuneCore, CD Baby, Amuse, Ditto and the rest all rely on similar audio-based AI detection, because they are ultimately answering to the same downstream platforms. The same is true one step further down the chain: if you want to understand the platform side of this, see Spotify AI detection, and if you are working with fully AI-generated material, our guide to clean AI-generated music goes deeper.
Lowering your track's AI score with a clean plus a proper master gives it the best chance to clear whichever distributor you use, without gambling your release date on an automated scan you can't see. Read more background in why distributors flag AI music.
Let's be clear about what this tool is for. artefactFX removes inaudible artifacts from your audio — the statistical residue that detectors react to. It is a mastering-adjacent cleanup step, not a way to launder someone else's work or dodge the rules. You are still responsible for following each distributor's and platform's AI policy, including any requirement to disclose that AI was involved in creating the track.
Used the right way, that is a genuinely good outcome for everyone: honest artists avoid false and borderline flags, disclose where they should, and release music they actually made. Cleaning your audio and disclosing your process are not in tension — do both, and you clear the gate with a clean conscience and a clean file.
artefactFX was built by people shipping real releases, not a generic audio utility. Detection uses professional AI analysis, cleaning targets the hidden fingerprint without wrecking your sound, and every result comes with a before/after score so you are never guessing. Check for free, clean only when you need to, and release with confidence.
Free check, one-click clean, before/after score.