Best AI Music Detector (2026): Eight Tools on One Test Set

We ran eight public AI-music detectors on the same 2,104 audio files at their fixed published thresholds. The honest answer is not a single winner — it depends on whether a false positive (real music flagged as AI) or a missed AI track costs you more. Here is the data, and who each tool actually suits.

How we tested

Every detector received the same restored ArtifactBench v1.1 files: 1,388 AI-generated tracks and 716 real tracks, identical inputs, each scored at its public fixed threshold of 0.5. This is a dated, reproducible snapshot — full methodology, preprocessing adapters and per-source results are in the companion field report and the public result record.

Disclosure: this benchmark was designed and run by Intrect, the maker of ArtifactNet — number one on F1 below. Read the report for the limits before you treat any figure as a purchase decision by itself.

The comparison table

Why you should care about false positives

A label, distributor or moderator reviewing a catalog does not only pay for missed AI tracks. Every genuine release incorrectly flagged consumes review time and can damage a creator's standing. In this run two systems with seemingly useful recall flagged more than two-thirds of real music — recall alone is a trap. FST made the opposite trade: the fewest false alarms, but it missed far more AI tracks.

The right tool is the one whose error profile matches your volume and tolerance. If you review thousands of tracks and one false positive is a real fight, a low-FPR tool wins. If you need to catch high volumes of AI music and can spend human time on triage, recall-and-precision balance matters more.

Who each tool suits

What an AI music detection tool actually measures

An AI music detection tool takes audio and returns a score for how likely the track is to have been generated rather than performed and recorded. What separates the tools above is not the score’s shape but what the score was trained and calibrated on, and that is what decides whether a flag is usable. A number you cannot act on is not evidence: if you cannot see which segment drove it, at what threshold, and how often the same model flags real music, a verdict leaves you with nothing to appeal, nothing to log, and no way to explain the decision to the person whose track it is.

Two limits are worth stating plainly. First, detection is not provenance — a score tells you a track resembles generated audio, not who made it or how, so it complements Content Credentials and platform metadata rather than replacing them. Second, every number on this page comes from a fixed set of tracks at a fixed threshold, so it describes those conditions and not your catalogue. If you want to judge a tool against your own audio rather than ours, run one file through the free in-browser analysis and compare what comes back with what you already know about the track. For volume, the same model is reachable over HTTP — see the /artifactnet/ API and batch endpoints.

Other AI music detectors you will see mentioned

Three names come up often in search results that are not in the eight-tool test above. We did not run them, so nothing here compares their accuracy with the table; the lines below only restate what each says about itself on its own page, read on October 3, 2026, and those pages can change.

• Deezer AI music detector: a free tool on Deezer's site that scans playlists from a streaming service (its page says up to 100 playlists) rather than a file you upload. Deezer states an accuracy figure of over 99%, but publishes no test set on that page. The “Deezer ISMIR fakeprint” row in our table is a separate public research model, not this product.
• ACRCloud AI Music Detector: a commercial service with a free trial and a sales contact, described by ACRCloud as covering full tracks, isolated vocals and accompaniment, and as naming the generator behind a track. It lists the generators it supports, such as Suno and Udio.
• SubmitHub AI Song Checker: a free checker on SubmitHub. Its page, as we could read it, gave no methodology or test results.

To judge any of them for your own catalogue, give each the same few files whose origin you already know and compare the verdicts, as the first FAQ below suggests.

What this comparison does not prove

• The restored real subset was 716 available files, not every row in the purged manifest — see the report's restoration boundary.
• A fixed threshold is reproducible but not necessarily optimal for your error budget.
• Generators and codecs change; a detector tuned to today's Suno/Udio may lag a new model. Query the maker for the generation they cover before you commit to volume.
• Detection is not provenance. No score replaces Content Credentials, platform metadata, or a human review-and-appeal path.

What detector can't do alone

Detecting AI music answers where a track may have come from. Fixing the audible artifacts it leaves on the audio is a separate job — this is what the de-artifact plug-in and online cleanup handle by removing RVQ ghosting and codec residue.

Sources and revision policy

Primary sources: the 8-way field report, the ArtifactBench dataset card, the public result table, and the ArtifactNet paper. This is a dated snapshot: a rerun gets a new report or an explicit correction, never a silent overwrite.

Try the top-F1 detector on your own file

Upload a track and get an AI-vs-human verdict with forensic evidence — free, in the browser, no account, nothing retained.