DistroKid flagged your song as AI, or you want to avoid it before you upload? Your track carries an inaudible AI fingerprint that DistroKid's screening scores. Check yours free, then strip the artifacts — with a before/after AI score so you can prove it worked.
Distributors moved fast once AI generators flooded the pipeline. DistroKid, like every major distributor, sits between you and the streaming platforms — and those platforms have started pushing back hard on undisclosed AI-generated audio, fraudulent uploads and catalogue spam. To protect its own standing with Spotify, Apple Music and the rest, DistroKid screens what comes through its intake, and part of that screening looks for audio that appears to be machine-generated.
The point isn't to punish anyone for using AI tools. It's that platforms now require disclosure and set their own limits, and a distributor that waves through a flood of unlabelled synthetic tracks risks its delivery relationship. So the scan happens at upload, and if your file scores as likely AI, it gets attention it wouldn't have gotten a year ago. Understanding that this is a risk-management scan, not a moral judgement, is the first step to dealing with it calmly and correctly.
The most important thing to understand is that the check is about the audio itself, not your metadata. It does not care what you typed in the artist field, whether you ticked an "AI" box, or what genre you selected. An automated detector extracts acoustic features from the waveform and returns a probability — a confidence score — that the audio was AI-generated.
That score comes from the fine-grained texture of how the sound was built. Neural synthesis leaves consistent statistical patterns that a real recording doesn't have:
Because it's a probability against a threshold and not a yes/no verdict, two things follow. First, a polished track and a rough one can score equally high — the tell is in the synthesis, not the songwriting. Second, you can measure exactly where your track sits before you ever upload it, using the free AI Checker, which reports the same kind of AI-probability score.
The consequences scale with the situation, and it's worth knowing them so you take the flag seriously without panicking. In the mildest case a release is held for review at intake, delaying your date. More firmly, a release can be rejected outright before it goes anywhere. If a track has already gone live and gets flagged afterwards — by the distributor or downstream by a platform — it can be taken down, which is far more disruptive because it can happen mid-campaign.
The most serious tier is account risk. Repeated flags, or patterns that look like abuse, can put your whole DistroKid account under scrutiny. None of this means "you can never release AI-assisted music" — it means undisclosed, high-scoring synthetic audio is exactly what the system is built to catch. The way to stay out of that loop is to check and clean before you upload, and to disclose AI where DistroKid or the destination platform requires it.
The instinct after a flag is to re-render the file and try again — bounce it, convert the format, normalise it, run it through a mastering chain. It almost never works, and the reason is simple: all of those steps repackage the same waveform. The fingerprint the detector scores on is baked into the samples themselves, so a new container, a new codec or a louder master preserves the underlying statistical pattern. You're handing the scanner the same audio in a different wrapper.
Mastering is the one people expect to work, because it changes the sound so audibly. But EQ, compression and limiting alter the surface of the audio while leaving the deep features a classifier relies on largely intact — sometimes a heavy master even bakes the artifacts in tighter, making them harder to isolate later. To actually move the score you have to process the signal to break up those statistical regularities, without introducing new artifacts the same model might read as suspicious. That's a targeted-processing problem, which is exactly what cleaning is for.
artefactFX splits the job into the two steps that actually matter: measure, then treat. First you check the AI-probability score with the free AI Checker, so you know where the track really stands rather than guessing. Then, if it's flagged high, the AI Cleaner strips the fingerprint with spectral, phase and temporal processing — breaking up the machine-regular patterns while keeping the track sounding like itself.
You get your file back as a studio-quality 24-bit WAV, not a re-compressed lossy file, plus a before/after score so you can confirm the fingerprint dropped before you re-upload to DistroKid. That last part matters: you never have to take it on faith, because you can see the number move and audition the cleaned audio yourself. If you're comparing plans or want to know what's included, everything is laid out on pricing.
The order you do things in matters. The most reliable route from a fresh export to a release that clears DistroKid's screening looks like this:
If you want to line up your key and tempo for the master or a remix while you're at it, the free BPM & Key finder reads both straight from the file.
Cleaning a finished stereo mix works, but cleaning stems works better. When every element is baked into one file, the processing has to treat vocals, drums, bass and synths together. Split them out and each part can be handled on its own terms — which matters because artifacts aren't spread evenly. Vocals, in particular, often carry the strongest AI signature of any element, so isolating and cleaning them individually usually moves the score the most.
If you have stems, upload them as a ZIP and you'll get each one back cleaned, ready to re-balance into your own mix and bounce a fresh master for DistroKid. If you only have the flat mix, cleaning that is still effective — it's just a coarser tool. Either way, always re-check the final bounced file, not just the individual stems, since the master is what you actually deliver.
One honest note, because it matters. artefactFX removes the acoustic artifacts that automated detectors score on — it does not remove any obligation you have to DistroKid or to the platforms you distribute to. This is not a tool for passing off fraudulent uploads or evading policy. Where DistroKid, a streaming service or a label requires you to disclose that a track uses AI, you should disclose it, and you should keep your use of your generator within its terms.
Used that way, cleaning does exactly what a legitimate producer needs: it stops an inaudible synthesis signature from getting a real, disclosed release throttled or held at intake, while you stay compliant with DistroKid's AI policy. Cleaning changes what a scanner measures; it doesn't change the rules you agreed to. If your release also touches other distributors or platforms, the same approach applies — see our guides to the TuneCore AI check and distributor AI checks in general.
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 deliver to DistroKid with confidence.
It's also honest about its limits. We won't tell you every track will magically pass — most drop well below the high-risk line after cleaning, a minority stay higher depending on the source, and mastering afterwards lowers the risk further. You see the real numbers at every step, on your own files. Releasing to streaming platforms too? The same approach applies on our Spotify AI detection page, and you can compare plans anytime on pricing.
Free check, one-click clean, before/after score. No sign-up to check.