Turn AI-generated tracks into release-ready audio. Remove the hidden artifacts that detectors flag, keep your sound intact, and get a 24-bit WAV with a before/after score.
When people say they want to "clean" an AI-generated track, they usually picture removing obvious noise — hiss, clicks, muddiness, or the metallic sheen some generators leave on vocals. Those things exist, but they are not what gets a track flagged. AI detectors do not listen the way you do. They score an inaudible statistical fingerprint that generative models bake into their output: subtle regularities in spectral balance, in the phase relationships between frequencies, and in the micro-timing of transients. You cannot hear this fingerprint, and no amount of EQ, limiting or hand editing reliably removes it.
Cleaning, in the artefactFX sense, means targeting that fingerprint directly while leaving the musical character of the song untouched. It is not mastering and it is not a remix. You upload a track, the cleaner reworks the underlying signal statistics that detectors key on, and you get back a 24-bit WAV pre-master plus a before/after AI score so you can see exactly how far the risk dropped. Think of it as peeling off a hidden layer that was sitting on top of your music the whole time. For a deeper technical walkthrough, see how to remove AI artifacts from music.
Generative music models are trained to reproduce the patterns in their training data, and in doing so they leave behind their own consistent signatures. A detector is essentially a classifier trained to recognise those signatures. It does not care whether the song is good, whether you edited it, or whether a human sang over it — it measures how closely the audio matches the statistical profile of machine-generated material and returns a probability, the "AI score" you see in our AI Checker.
The frustrating part for producers is how durable that fingerprint is. It survives the things you would expect to scrub it:
That is why simply "mixing it more" or running a track through a mastering plugin usually is not enough to move a high-risk track into the clear. The signature lives in the fine structure of the signal, below the level your everyday tools operate on.
The AI Cleaner applies a combination of spectral, phase and temporal processing designed to be transparent to the ear. Rather than notching out frequencies or adding noise, it reshapes the statistical distribution of the signal so that the tell-tale regularities detectors look for are broken up, while the tonal balance, groove and dynamics a listener responds to stay intact.
Every clean produces the same deliverables: a studio-quality 24-bit WAV pre-master, and a before/after AI score so you never have to guess whether it worked. Most tracks drop well below the high-risk line after a single pass. Some — depending on the source generator and how heavily processed the original was — stay higher, which is exactly why we show you the number instead of just claiming success. You clean, you re-check, and you decide.
The processing is built to preserve musical character, not to trade quality for a lower score. Because the target is the inaudible fingerprint rather than anything you actually listen to, a well-behaved clean should sound like the same song. But you do not have to take that on faith — the whole point of returning a 24-bit WAV alongside a before/after score is so you can judge for yourself. A/B the original against the cleaned file on good monitors or headphones, and if anything about the character changed in a way you dislike, you will hear it immediately.
Starting from a high-quality source helps here too. A lossless WAV or FLAC gives the cleaner clean data to work with; a low-bitrate MP3 already has lossy artifacts of its own that limit how good the output can be.
The cleaner is generator-agnostic. It targets the shared statistical artifacts that generative audio models leave behind, not one vendor's specific quirks, so it works across tools:
In practice, vocals tend to score highest — synthesised or heavily model-processed voices carry a strong fingerprint — followed by lead melodic elements. Drums and simple instrumental beds often score lower to begin with. That pattern is useful to know, because it tells you where to focus when a track comes back still reading high.
Cleaning individual stems is more thorough than cleaning a finished mix. When elements are separated, the fingerprint on each one can be targeted directly, without the compromise of treating a full mix as a single blended signal — and it lets you clean the high-scoring parts (usually vocals) harder than the parts that were already fine. If you have stems, upload them as a ZIP. If you only have the finished master, a full-mix clean still works well and is the faster path.
For a fuller comparison of the trade-offs, read stems vs full mix, and see making AI music sound human for the finishing touches that help a track read as a genuine production.
The order matters. Always begin from the highest-quality file you have — ideally the lossless export straight from the generator, not a re-downloaded MP3. Check first so you are not spending a clean on a track that already reads low. After cleaning, re-check to see the new score, and only then master. If a track is heading for distribution, this same pipeline is what helps it pass distributor AI checks. Sorting out tempo and key while you are at it? The free BPM & Key finder handles that in the browser.
Cleaning removes the technical artifacts that cause false-positive AI flags and quality complaints. It does not change your obligations. Platforms, distributors and stores set their own rules about AI-generated and AI-assisted content, and those rules change often. It is on you to read them and to disclose the use of AI where it is required. We help your music be judged on its merits rather than getting caught by a blunt automated filter; we do not help you misrepresent how a track was made. Treat the AI score as a quality-assurance signal, not a loophole.
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.
Remove the artifacts, keep your sound, get a before/after score.