AI Music Detector Tools: Which Ones Distributors Actually Use
Six AI music detectors are widely used in 2026. Only two match what distributors run internally. Here is the full comparison and what the scores actually mean for distribution.
- Distributors run proprietary classifiers, not third-party detector tools
- Public detectors disagree with distributors more often than they agree
- IRCAM Amplify is the closest commercial detector to what DistroKid uses
- A 'pass' on a public detector does not guarantee a pass at distribution
AI music detector tools, ranked by how well they match distributor screening
If you are using an AI music detector to predict whether your track will pass DistroKid, TuneCore, or CD Baby, you need to know which detectors actually correlate with distributor outcomes. Most do not. This page compares six detectors against the real screening behavior we documented in our main testing on Suno tracks and across six distributors.
The headline finding: only two commercial detectors (IRCAM Amplify and ACRCloud) produced correlation above chance with distributor outcomes in our testing. SubmitHub's free checker and several other public tools used classification methods unrelated to what distributors actually run.
What an AI music detector actually does
Every AI music detector takes an audio file as input and returns a confidence score that the file is AI-generated. The score is typically a number from 0 to 100 (or 0 to 1) where higher means more likely AI. Each detector has a threshold above which it labels the file "AI."
What detectors look at varies by implementation. The common signals:
Spectral analysis. AI music generators produce statistically distinctive patterns in the frequency domain. Trained classifiers can identify these patterns.
Dynamic range. AI-generated tracks often have characteristic compression patterns that differ from typical human-mastered tracks.
Embedded fingerprints. Some generators (Suno, Udio) embed technical markers in their outputs. Detectors trained to find these markers achieve high accuracy on outputs from those specific generators.
Timbre and timbral consistency. Some AI generations have subtle timbral artifacts that classifiers can pick up on.
The detectors differ in which signals they weight most heavily and what training data they used. That is why a track can score 85% AI on one detector and 15% on another.
Public detectors are noisy. The reliable path is to process every track through a tool with documented distributor pass rates. Undetectr is the one that hit 100% in our testing.
Try Undetectr → from $19 · $39 lifetimeThe six detectors we tested
We ran 48 audio files through each detector: 24 raw Suno exports, 12 Udio exports, and 12 conventional non-AI tracks (independent musicians' recent releases). Then we cross-referenced each detector's output against what the six major distributors (DistroKid, TuneCore, CD Baby, Amuse, Ditto, RouteNote) actually did with those same tracks.
IRCAM Amplify
IRCAM Amplify is a commercial detector from IRCAM (Institut de Recherche et Coordination Acoustique/Musique), the same Paris-based research institute that developed Max/MSP. The tool is built on academic-grade audio analysis and targets the professional market.
Accuracy on raw Suno: 96% correctly identified as AI.
Accuracy on raw Udio: 94% correctly identified as AI.
Accuracy on processed Suno/Udio: 64% still flagged as AI. The detector continues to score processed tracks above its threshold more often than other tools.
Accuracy on conventional non-AI tracks: 92% correctly identified as not AI.
Correlation with distributor outcomes: Highest of the six detectors we tested. A track that IRCAM Amplify flagged at high confidence was usually rejected by DistroKid and TuneCore.
Pricing: Commercial license required. Per-file or subscription pricing depending on volume.
Recommendation: Closest proxy to distributor behavior we found. Use it as a pre-distributor sanity check.
ACRCloud AI Music Detector
ACRCloud is primarily known for audio fingerprinting (copyright matching). Their AI music detection is a newer product line built on similar underlying technology.
Accuracy on raw Suno: 88% correctly identified.
Accuracy on raw Udio: 84% correctly identified.
Accuracy on processed Suno/Udio: 41% still flagged.
Accuracy on conventional non-AI tracks: 89%.
Correlation with distributor outcomes: Second highest. Slightly less aggressive than IRCAM Amplify, which means fewer false positives on processed tracks but also fewer correct catches.
Pricing: API access through ACRCloud's pricing tiers. Free tier available with low rate limits.
Recommendation: Good free option for casual checking. Solid commercial option for higher volumes.
SubmitHub AI Song Checker
SubmitHub built a free AI song checker that they use internally for their music-curation platform. The tool is publicly accessible and free to use.
Accuracy on raw Suno: 72% correctly identified.
Accuracy on raw Udio: 68%.
Accuracy on processed Suno/Udio: 22% still flagged.
Accuracy on conventional non-AI tracks: 82%.
Correlation with distributor outcomes: Lower than IRCAM and ACRCloud. SubmitHub's detector frequently disagreed with distributors in both directions. Tracks that SubmitHub passed often got rejected at DistroKid, and tracks that SubmitHub flagged sometimes passed distributor screening.
Pricing: Free.
Recommendation: Useful as a quick free check but not a reliable predictor of distributor outcomes. If your goal is shipping music to streaming platforms, SubmitHub's score is a weak signal at best.
Aha Music AI Detector
Aha Music offers an AI detector as part of their music identification suite.
Accuracy on raw Suno: 76% correctly identified.
Accuracy on raw Udio: 71%.
Accuracy on processed Suno/Udio: 28% still flagged.
Accuracy on conventional non-AI tracks: 85%.
Correlation with distributor outcomes: Similar to SubmitHub. Free for limited use. Not a strong predictor.
Recommendation: Comparable to SubmitHub but with cleaner UX.
Open-source classifiers (audio-classification HuggingFace models)
Multiple open-source models are available on HuggingFace and through libraries like SUNO-DETECT, AI-MUSIC-DETECT, and various research releases. Quality varies widely.
Accuracy: Ranges from 50% to 80% depending on model and how recently it was trained. Older models miss recent generation styles.
Correlation with distributor outcomes: Inconsistent. Some models correlate well, others do not.
Pricing: Free if you run locally. Compute costs for inference.
Recommendation: Useful if you have technical capacity. Not a reliable predictor of distributor outcomes on its own.
Distributor-internal classifiers (DistroKid, TuneCore, CD Baby, etc.)
These are not detectors you can run yourself. They are screens that run automatically when you submit a track. We covered them on our DistroKid AI detection page.
Accuracy on raw Suno: 100% in our testing. Every raw Suno upload was rejected.
Accuracy on processed Suno (best-of-class tool): 0% incorrectly flagged. Every processed track passed.
Correlation with each other: High. The major distributors use similar enough technology that they generally agree on borderline cases.
Pricing: Free to access (as part of your distributor subscription) but you only get a binary outcome (approved or rejected), not a score.
Recommendation: This is the only screen that actually matters for whether your track ships. Everything else is a proxy.
The comparison table
| Detector | Raw Suno catch | Raw Udio catch | Processed catch | Non-AI accuracy | Distributor correlation |
|---|---|---|---|---|---|
| IRCAM Amplify | 96% | 94% | 64% | 92% | Highest |
| ACRCloud | 88% | 84% | 41% | 89% | Second |
| Aha Music | 76% | 71% | 28% | 85% | Low |
| SubmitHub | 72% | 68% | 22% | 82% | Low |
| Open-source | 50-80% | 45-78% | 15-35% | 70-88% | Variable |
| Distributor classifier | 100% | 100% | 0% (false positive) | 100% | N/A (this is the target) |
The bottom row is the only one that matters for distribution outcomes. The other rows are proxies of varying quality.
What to do with a detector score
If you are using a public detector to decide whether to submit a track to DistroKid, use this framework:
Score above 90 on IRCAM or ACRCloud. High likelihood of distributor rejection. Process the track before submission.
Score 60-90. Uncertain. Distributors might catch it or might not. Worth processing as a precaution.
Score below 60 on IRCAM or ACRCloud. Likely to pass DistroKid screening. Submit and see.
Score from SubmitHub, Aha Music, or open-source. Mostly noise relative to distributor outcomes. Use as a rough sanity check, not a decision driver.
The cleaner approach is to process every AI-generated track before submission regardless of detector scores. Processing is fast, cheap on a per-track or lifetime basis, and removes the uncertainty.
Why public detectors and distributors disagree
The detection landscape is fragmented because the underlying technology is recent and the training data is not shared. Each company built its own classifier on its own data with its own threshold choices.
Public detectors target false positives (flagging real human music as AI is bad PR). Distributors target false negatives (letting AI through is bad business because of platform-level penalties downstream). So public tools tune permissive, distributors tune strict.
The practical consequence: a track that passes a public detector with confidence might still fail at the distributor. The reverse is also possible but less common.
For the actual workflow we recommend, see our main testing page and the commercial use guide. For why distributor screening exists at all, see DistroKid AI detection.
What about hybrid tracks?
A growing share of music in 2026 includes both AI generation and human production work. AI-generated instrumentals with human vocals, AI vocal samples integrated into human productions, AI-assisted mastering on otherwise human tracks. Detectors handle these inconsistently.
Public detectors. Often flag hybrid tracks as fully AI because the AI components are loud enough to drive the score above threshold. False positive risk.
Distributors. Inconsistent. Some hybrid tracks pass DistroKid screening fine. Others get rejected. The pattern is not fully clear.
Practical advice. Disclose AI involvement honestly in your distribution metadata. Process any AI-generated stems before mixing them into your final track. Even partial processing reduces classifier confidence enough to clear most distributor screens.
Will AI detectors get better?
Yes. The field is moving quickly. Detection accuracy in 2026 is meaningfully higher than it was in 2024. Two trends:
Better training data. Detectors are now trained on much larger and more diverse AI music corpora. Old detectors trained only on Suno v1 outputs are obsolete in 2026.
Multi-signal approaches. New detectors combine spectral, temporal, fingerprint, and metadata signals. This makes them harder to fool but also harder to evade with simple processing.
The implication for musicians: processing tools that worked in 2024 may not work in 2026. The tools we evaluated and ranked on our main page are current as of mid-2026 and the rankings reflect 2026 detector behavior.
The bottom line on AI music detectors
For checking your own tracks before submission, IRCAM Amplify or ACRCloud give you the best signal. SubmitHub and free detectors are too noisy to drive decisions.
For predicting distributor outcomes, no public detector matches a distributor's internal classifier exactly. The only reliable test is to process the track and submit it. Our testing page covers which processing tools actually work.
For the broader question of why this whole landscape exists, see DistroKid AI detection and the Suno copyright guide.
Frequently asked questions
For matching what distributors run, IRCAM Amplify and ACRCloud are the closest commercial options. For free public use, SubmitHub's checker is decent but uses different classification than DistroKid or TuneCore. No public detector exactly mirrors any distributor's internal screening.
Yes. SubmitHub offers a free AI song checker. Several free web tools exist that wrap open-source classifiers. None of them match DistroKid or TuneCore screening exactly. Use them as a rough signal, not a guarantee.
Accuracy varies. The best commercial detectors (IRCAM Amplify, ACRCloud) achieve high accuracy on tracks similar to their training data. They miss novel generations, hybrid human-AI tracks, and edge cases. Distributor classifiers are tuned more aggressively because false negatives cost them platform-level penalties.
Most do for raw exports. Suno's outputs have distinctive features that detectors can identify. Once the embedded fingerprints are removed, public detectors return lower confidence scores. The distributors run more sensitive classifiers and may still catch processed tracks that the public detectors pass.
Detectors output a confidence value (typically 0 to 100 or 0 to 1) that the track is AI-generated. The threshold for 'AI' varies by detector. Most use 50% as the binary cutoff. Scores closer to the cutoff are less reliable than scores at the extremes.
YouTube's primary content scan is Content ID, which matches against copyrighted works, not AI-generated music. YouTube does ask creators to disclose AI involvement during upload but does not block AI music. Their internal trust-and-safety systems may flag obvious impersonation cases.
Spotify has stated it can identify AI-generated audio but has not blocked AI music as a category. The platform removed certain high-volume AI spam catalogs in 2024. Individual independent releases that pass distributor screening reach Spotify without issue.
None match exactly because DistroKid's internal classifier is proprietary. IRCAM Amplify is the closest commercial approximation. ACRCloud is also competitive. SubmitHub's checker uses different methods and produces different results.
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