AI music detectors: how detection works and what platforms do about it

Deezer reported receiving close to 75,000 fully AI-generated tracks a day by mid-2026, more than 44 percent of everything delivered to it. A year earlier that figure was around 18 percent. Whatever you think of the trend, the response to it is now a real system with real consequences: detection, tagging, and exclusion from recommendations.

If you release generated music, this is the part of the landscape that affects you most directly, and it is barely discussed compared with the copyright lawsuits.

What detection actually looks for

Three mechanisms, in descending order of how much work they do today.

Spectral and temporal artefacts. This is the main method. Generative audio pipelines leave measurable fingerprints: vocoder banding, unnatural phase coherence, and systematic spectral peaks at predictable intervals produced by the deconvolution layers inside neural audio generators. Research presented at ISMIR in 2025 demonstrated that last effect specifically. Because different generators leave different signatures, platform-scale detectors can often name which tool produced a track rather than only flagging that one did.

Ensembles. The systems that work best combine several detectors, each tuned to a different generator, and weigh the results.

Watermarking and provenance. Two standards are converging: C2PA attaches cryptographically signed metadata documenting authorship and AI involvement, and Google's SynthID embeds imperceptible marks in the waveform that survive compression and format conversion. The EU's code of practice on AI content labelling has been pushing this direction. Watermark-based verification is far more reliable than artefact analysis, because it is a signature rather than an inference.

How accurate is it, honestly

Reported accuracy on professionally produced tracks sits roughly in the 85 to 93 percent range for artefact-based detection, with watermark verification approaching certainty under controlled conditions. Deezer claims 99.8 percent for its own system.

The caveats matter more than the headline:

False positives are real. Heavy mastering, vocoder-heavy human vocals, and lossy re-encoding can all trip a detector. A human singer through a lot of processing can look synthetic to a spectral analyser.

Hybrid tracks are the hard case. A human vocal over generated instrumentation, or generated material heavily edited by a person, sits in the gap these systems handle worst.

Detection is screening, not proof. A confidence score is not evidence of anything in a legal sense. Treat a flag as a reason to look, not a verdict.

What the platforms actually do about it

Policies diverge sharply, and knowing which platform does what changes where you release.

Deezer tags AI-generated tracks, excludes them from algorithmic recommendations and editorial playlists, and lets listeners filter them out. It reported that a large majority of streams on fully AI-generated songs were identified as fraudulent and demonetised, with those streams excluded from royalty calculations. In June 2026 it released its detector as a free public tool covering playlists on around twenty other services, and offered the technology to rival platforms.

Spotify and Apple Music have leaned toward labelling rather than removal, without a user-facing filter.

Bandcamp bans AI-generated music outright.

Tidal encourages disclosure while building internal detection.

YouTube Music uses automated systems without specific AI tagging.

The practical read: fully generated, undisclosed, mass-uploaded tracks are being actively deprioritised, and the economics of volume-dumping have collapsed accordingly. Why that business model fails now has an enforcement mechanism behind it, not just poor per-stream rates.

What this means if you release generated music

Five things, and none of them are "hide it."

Disclose. The trend across platforms, distributors and the EU code of practice is toward mandatory labelling. Disclosing voluntarily costs you very little now and protects you when it becomes required.

Expect exclusion from algorithmic promotion on some platforms, and plan a route to listeners that does not depend on it.

Add human contribution, and not only for detection. Your own lyrics, your own arrangement decisions, your own recorded parts, real mixing. This matters twice: it makes the track less purely synthetic, and under the US Copyright Office's position it is the only thing that gives you a copyright in the result, because prompting alone is not authorship. It also happens to be where the quality is: a specific brief plus deliberate arrangement produces something that sounds intentional rather than averaged, which is what our style catalogue is built to make repeatable.

Do not process audio to defeat detection. It degrades your track, it is likely to fail against watermarking, and it converts an honest labelling question into a deception question.

Keep your provenance records. What was generated, when, on which plan, with what prompt. If you are ever asked, that documentation is worth more than any argument.

Checking a track yourself

If you want to know whether something you found is generated, the practical approach:

Use more than one detector. Single-model results are noisy. Agreement between two is meaningfully stronger evidence.

Ask for a vocals-versus-instruments breakdown where the tool offers one, since that is what exposes hybrid tracks.

Listen for the tells. Detectors are catching what your ears can sometimes catch too: a high-frequency haze, vocal consonants that smear, reverb tails that behave oddly, and arrangements that repeat with unnatural precision.

Do not treat the result as proof. Especially not publicly, about someone else's work. False positives on heavily processed human recordings are common enough that accusations age badly.

FAQ

Can AI-generated music be detected? Usually, through spectral artefacts left by generative pipelines, with reported accuracy around 85 to 93 percent and higher where watermarking is present.

Is there a free AI music detector? Deezer released one publicly in June 2026, covering playlists across around twenty streaming services.

Will Spotify remove my AI-generated music? Its stance has been labelling rather than removal, unlike Deezer, which excludes tagged tracks from recommendations. Policies move, so check current terms.

Do I have to disclose that a track is AI-generated? Increasingly yes, depending on platform and territory, and the direction of travel is clearly toward mandatory labelling.

Can detection be fooled? Artefact analysis can be degraded by processing. Watermark-based verification is far harder to defeat, and trying is a bad idea regardless.

Does human editing change the result? It complicates detection and, more importantly, it is what earns you a copyright in the finished work.