People search for an AI music checker, an AI music detector and an AI music tester looking for the same thing: put a song in, find out whether a machine made it. Search pages treat them as separate products. Behind the words, the job is one job.
We run a free detector, so it is worth saying plainly what these tools can and cannot do, and which of the three words you should actually type.
The short answer
Short answer. A checker, a detector and a tester all return an estimate of how likely the audio is AI-generated. None of them returns a fact. Pick a tool by what it shows you, a verdict with a probability, a segment timeline and the features behind the score, not by the word on the page.
One job, three words
Every tool in this category takes an audio file and runs a model over the waveform. The model looks for what generation leaves behind: unstable high-frequency detail, spectral residue that does not behave like a recording, a U-Net mask that predicts synthetic content per frame. The result is a probability between 0 and 1.
The words differ in what they imply, not in what they compute:
- Checker implies a pass or fail, like a spelling checker. Real tools do return a verdict line, but the verdict is a threshold applied to a probability, and the threshold is a choice.
- Detector implies measurement. This is the word the research literature uses, and the one that fits a score with published evaluation behind it.
- Tester implies a test suite, several runs, a result you can compare. Most tools on a search page do not offer that; they return one number for one file.
If a page promises certainty, the word is doing more work than the model can. Any tool that says it proves authorship has gone past what the method supports.
What a useful result contains
A probability alone is hard to act on. Four pieces make it usable:
- A verdict with its threshold. The demo labels a track as flagged, not flagged or partly flagged, and draws the line at 20% probability. Below that line, the tool is saying the evidence is weak, not that the track is human.
- The probability itself. 2.8% and 19% both sit below a 20% line, but they are not the same reading.
- A segment timeline. Detection runs in 4-second windows, so one track can hold flagged and unflagged stretches. A single mean hides that: a track with two generated sections and six clean ones is not the same problem as a fully generated one.
- The features behind the score. High-frequency residual, consistency across windows and the U-Net mask tell you whether the score rests on one cue or several.
The free ArtifactNet demo shows all four on your first analysis of the day. That is the part worth comparing when you shop for a checker: not the name, the detail.
What a score cannot tell you
A detector reads audio, and audio carries less information than people assume.
- It cannot tell you who made the track. A score describes the file you uploaded, not authorship, and not the share of a song that a model wrote.
- It can be wrong in both directions. False positives land on clean, heavily processed or unusual recordings; false negatives land on generated audio that has been mixed, mastered or re-encoded. MP3 is supported, but lossy encoding changes the high-frequency detail the model reads, so the same track can score differently as WAV and as MP3.
- It reads only part of a long file. The anonymous demo analyses the first 2 minutes. A track that turns synthetic at 3:10 will read clean if the opening is human.
The honest framing is the one the panel itself uses: a score is evidence for review. Put it next to the release history, the stems, the project file, the distributor's metadata. A score decides who to ask, not what happened.
Check one track, free
No account, no card. Upload an MP3, WAV or FLAC and read the verdict, the probability and the segment timeline. The first analysis of the day shows the full panel.
What the free demo allows
- Formats: MP3, WAV, FLAC, OGG, OPUS, M4A, AAC or WMA, up to 50 MB per file.
- Limits: 2 minutes per track and 6 minutes in total for one anonymous visitor. No signup and no card to start.
- Upload only: the demo used to fetch audio from a URL, and that was withdrawn. Export the file and upload it.
- Retention: nothing you upload is stored unless you submit a report.
- Free account: signing in keeps the segment timeline and the feature panel on every analysis, not just the first of the day, and adds more free minutes.
Start with the Free AI music detector and one track: the list above is the whole free tier, so there is nothing to unsubscribe from.
For batch work, distributors, labels and streaming platforms can call the same model over the ArtifactNet REST API instead of the page. The detection method and its limits are documented in how to detect AI-generated music.
Which word should you search for
Use the word that matches what you want:
- Checker if you want a plain answer on one file and do not care how the tool reached it.
- Detector if you care about method, thresholds and error rates; this is the word the research uses, and the one that will find published evaluations.
- Tester if you want to try several files and compare, which most free pages do not support well: watch for per-visitor limits, since a tester that stops after six minutes is not a test suite.
Then judge the result, not the label. Two pages can both say "AI music detector" and return wildly different evidence: one a single percentage, the other a probability per 4-second window with the features behind it. The second is the one you can argue with.
Cleanup is a separate question
Detection tells you what a file may be. It does not tell you what to do next. If the track is yours and the problem is the sound, that is artifact removal: metallic tails, watery vocals, fizzy cymbals. If the problem is that a distributor or a client asked about the file, the answer is the evidence trail, not a higher score.
Keep the two apart. A cleaner does not make a track human, and a detector score does not tell you whether a cleaner helped. Judge cleanup by ear at matched loudness, the way our tool comparison does.
FAQ
Are an AI music checker, a detector and a tester the same thing?
In practice yes. All three take an audio file and return an estimate of how likely it is that the audio was generated by an AI model. The words come from marketing, not from a difference in method, so compare what a tool returns, not what it calls itself.
Does a high AI score prove a song was made by AI?
No. A score is evidence for review, not proof of authorship. Detectors can miss AI audio and can flag human recordings, and the score describes the audio that was submitted, not who made it. Compare the result with the release history, stems or project files before you act on it.
What does the free ArtifactNet demo include?
Upload an MP3, WAV, FLAC, OGG, OPUS, M4A, AAC or WMA file of up to 50 MB: 2 minutes per track and 6 minutes in total, no signup. The first analysis of the day shows the segment timeline and the forensic feature panel; a free account keeps them on every analysis.
Can I check a song from a link?
Not from a link. The demo used to fetch audio from a URL and that was withdrawn. Export or download the track yourself and upload the file. Nothing you upload is stored unless you submit a report.
Does MP3 affect the result?
It can. MP3 is supported, but lossy encoding changes high-frequency detail, which is part of what a detector reads. When you have a choice, upload the earliest lossless file you have.