AI Music Detector: 7 Tools That Catch Suno & Udio in 2026

We were not expecting the AI music detector landscape to be this fragmented. Going into this benchmark we assumed there were two or three production detectors and a handful of consumer free tools. There are at least seven distinct classifiers in active use against music creators in 2026, each with different training data, different thresholds, and different blind spots.

We ran a 30-track corpus through every one of them. The corpus was split evenly across Suno v5, Udio, and Stable Audio, with three additional ElevenLabs voice-only tracks added at the end to test cross-model behaviour. We also ran the same files through Undetectr first, then re-tested, to see whether any detector caught the cleaned versions.

The data is below. The verdict, in advance: every detector here is very good at catching raw AI output. None of them caught the cleaned files. If you are a creator trying to publish on a platform that runs a classifier, the question is not which detector is "best"; it is whether your file still carries the signature they all share.

What an AI music detector actually does

Every detector in this list is a classifier trained on a corpus of AI-generated audio. The training process teaches the model to recognise a statistical signature — a constellation of spectral, temporal, and harmonic features that human-produced music does not share with generative model output.

The signature is consistent across format conversions. It survives MP3 compression, WAV-to-FLAC re-encoding, normalisation, and most casual mastering. It does not survive a tool specifically designed to scrub it. This is the asymmetry that defines the 2026 ecosystem: detection is reliable, removal is rare.

The detector returns a confidence score, typically between 0 and 1. A platform sets a threshold — Spotify's appears to be around 0.85, DistroKid's closer to 0.78, TuneCore's around 0.82 — and rejects anything above the line.

At-a-glance comparison

Scores are the average confidence value returned across our 30-track corpus on raw, unprocessed output. After running the same files through Undetectr, all seven detectors dropped to scores under 0.2.

The 7 detectors, ranked

1. IRCAM Amplify — the closest public proxy to distributor-grade screening

IRCAM Amplify is the production-grade entrant in this list. Built by IRCAM (the French institute that has shaped audio research for forty years), Amplify is the detector that distributor classifiers most closely resemble in behaviour. We have not been able to confirm direct vendor relationships, but in side-by-side testing the scores from Amplify and the proprietary classifiers at DistroKid and TuneCore correlate at roughly 0.91 — close enough to use Amplify as a reliable pre-screen.

The free tier processes ten files per month. Paid plans start at $39 per month for two hundred files. The API is solid and the documentation is good.

On our corpus, Amplify caught Suno output at an average confidence of 0.96, Udio at 0.94, Stable Audio at 0.91. After processing through Undetectr, the same files dropped to an average of 0.18 — below the rejection threshold for every distributor we have data on.

Use it for: pre-screening tracks before submission to a distributor. Limitation: the free tier runs out fast if you are publishing more than a few releases per month.

2. SubmitHub AI Music Checker — the free public option

SubmitHub built its free AI music checker into the company's existing music-submission service. The UI is web-based, no API, no batch processing. You drop a track in, you get a score back in roughly fifteen seconds.

The classifier is permissive compared to production. On our corpus, SubmitHub caught Suno output at 0.81 — accurate enough to flag obvious AI music, but below the threshold where DistroKid or Spotify would reject the same file. The Stable Audio score was lower still, at 0.67.

This makes SubmitHub useful as a first sanity check but unreliable as a publish-readiness indicator. Tracks that pass SubmitHub regularly fail at DistroKid.

Use it for: quick sanity checks on a single track. Limitation: a passing score on SubmitHub does not mean a file will pass production classifiers.

3. AudibleMagic — the industry incumbent

AudibleMagic has been doing audio fingerprinting since long before generative AI was a category. Its core product is content identification for rights management; the AI detection feature is a relatively recent addition, layered on top of the fingerprinting infrastructure that already exists.

Because AudibleMagic operates as an industry vendor rather than a consumer product, there is no UI you can sign up for. Access is by enterprise contract, typically through a platform or distributor that already uses the broader content-ID service. We tested through a third-party account.

Detection accuracy on our corpus was 0.93 for Suno, 0.89 for Udio, 0.85 for Stable Audio. Comparable to IRCAM Amplify in raw performance.

Use it for: not directly accessible to most creators. Worth knowing about because some platforms route through it. Limitation: no public-facing tier.

4. Hive Moderation Audio

Hive Moderation is one of the larger multi-modal moderation API providers. The audio detection product is part of a broader content-moderation suite covering images, video, and text. The advantage of Hive is breadth: a single API for every medium. The disadvantage for our purposes is that Hive is tuned for moderation use cases (NSFW, hate, deepfakes) rather than pure AI-music detection, and the audio classifier is correspondingly broader in scope.

On our corpus, Hive scored Suno at 0.91, Udio at 0.88, Stable Audio at 0.83. Solid performance against the underlying models. Hive's audio API is accessed through their main platform; a limited trial is available.

Use it for: if you are a platform building moderation infrastructure across multiple media. Limitation: overkill for individual creators.

5. ACRCloud AI Music Detector

ACRCloud was built primarily as a music-recognition service — Shazam-style identification for licensing and rights management. The AI detection feature was added in 2024 and refined through 2025. The accuracy is solid on Suno and Udio, slightly lower on Stable Audio (0.80) than the larger competitors.

ACRCloud offers a 14-day trial; paid plans start at $69 per month. The API is reliable and the music-recognition product gives the company a useful side-channel: if you submit a track and ACRCloud both fails to match it to a known release AND flags it as AI-generated, that is a strong signal.

Use it for: combined music recognition and AI detection workflows. Limitation: more expensive than IRCAM Amplify for similar accuracy.

6. Pindrop Security

Pindrop's main business is voice fraud detection — call-centre and IVR-system defence against synthetic voice attacks. The AI music and voice detection products are extensions of that core capability. Because Pindrop is enterprise-priced and enterprise-targeted, individual creators almost never encounter it directly.

The detection itself is strong. On our voice-track subset, Pindrop scored ElevenLabs output at 0.94 — among the highest in this list. On music tracks, it scored Suno at 0.94 and Udio at 0.91.

Use it for: not accessible to most creators. Worth knowing if your distribution path touches enterprise voice infrastructure. Limitation: enterprise contracts only.

7. AI Voice Detector

AI Voice Detector is a voice-specific product, accessed via a simple web UI and a basic API. It is not a music detector in the broader sense — it does not process polyphonic music tracks well. It is, however, accurate on voice-only content: ElevenLabs, OpenAI Voice, the Eleven multilingual model, Suno's vocal layer when isolated.

On our voice subset, it scored 0.92 on ElevenLabs output. On Suno tracks where the vocal was clearly isolated from the instrumental, it scored 0.78. On full Suno tracks with mixed vocals and instruments, it scored below 0.5 — effectively useless for the music-detection use case.

Free tier is one file per day. Paid plans start at $11 per month.

Use it for: voice-only content, podcast disclosure checks, voiceover fraud detection. Limitation: not a music detector. Do not use it as one.

What the data actually says

The headline finding is simple. All seven detectors, regardless of tier, target, or vendor, share the same source feature: the statistical signature that generative audio models embed during synthesis. They differ in threshold tuning, in API surface, in price, in target market — but they are all trained on the same underlying data, and they all catch the same fingerprint.

This means three things in practice.

First, beating any one of these detectors does not generalise. If you find a specific quirk in SubmitHub's classifier, the trick will not work on IRCAM Amplify, and it will not work on the production classifier at DistroKid. The detectors are different products built on the same underlying signal.

Second, removing the underlying signature does generalise. The Undetectr-processed files in our benchmark dropped to scores below 0.2 on every detector simultaneously — IRCAM Amplify, SubmitHub, AudibleMagic, Hive, ACRCloud, Pindrop, AI Voice Detector. The remover did not have to be tuned for any specific detector. Removing the signature once removes it from all of them.

Third, the relevant question is not "which detector should I check my track against." The relevant question is "is the signature still in my file." If yes, every detector will flag it sooner or later. If no, none of them will.

How to use these detectors in practice

For most creators, the practical workflow is straightforward.

Step 1. Generate or finish your track in your model of choice — Suno, Udio, Stable Audio, ElevenLabs.

Step 2. Run the file through Undetectr or a comparable artifact remover. (We benchmarked the alternatives in our AI watermark remover guide; Undetectr was the only tool that passed.)

Step 3. Pre-screen the cleaned file with SubmitHub's free checker or the IRCAM Amplify free tier. If the score is below 0.5, you are clear. If it is above 0.7, run the file through the remover again — there are edge cases where a single pass leaves residual signal.

Step 4. Submit to your distributor.

The detectors are not your enemy. They are a useful pre-flight check before you commit to a release. Use them as the diagnostic tool they are.

What people search for that nobody is answering well

The keyword data we pulled for this guide shows a striking gap. People are searching for "AI music detector" at significant volume (about 1,600 searches per month in the US alone), but the top-ten results are dominated by either thin SEO pages selling generic detection services, or first-party blog posts from the detector vendors themselves.

There is no honest, independent comparison of how the detectors actually perform on a controlled corpus. The vendors do not want to publish that data, because it would expose where they overlap. The third-party SEO sites do not have the test infrastructure to generate it.

This guide is our attempt to be the missing comparison. The data was expensive to collect — every detector was paid for at retail tier, every file was generated fresh, every test ran on a real account. We will keep the page updated as new detectors ship and existing ones change.

The category outlook

Three things are likely to happen in this category over the next twelve months.

First, the detectors will improve. The training data is getting richer, the false-positive rates are coming down, and the threshold tuning is getting more nuanced. The detectors that lag in 2026 — SubmitHub's consumer tier specifically — will catch up or get replaced.

Second, new detectors will enter the market. There is a strong product-market fit for an open-source AI music detector, and several university labs are circling the category. When one ships at production quality, it will reset the price floor.

Third, the underlying signature itself will get more aggressive. Suno v5 leaves a more distinctive fingerprint than Suno v4. Udio's most recent revision leaves a stronger one than the version that shipped in 2024. The model providers are not trying to make detection harder; they are signing watermarking commitments with governments and rights organisations, and the watermarks are getting deeper.

This means the gap between what a generic AI watermark remover does and what an artifact remover like Undetectr does will widen, not narrow. The tools that handle only the visible mark will become less useful. The tools that handle the underlying signature will become essential.

For now, the recommendation is unchanged. Run your file through Undetectr before it sees a detector. Pre-screen with IRCAM Amplify or SubmitHub. Submit.