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For most listeners, start with Deezer’s free AI Music Detector. It can scan playlists from 20 common streaming platforms and is the easiest way to screen a suspicious release. Use Google SynthID Detector when the audio may have been generated or published through Google’s supported watermarking systems. Labels, distributors, publishers and platforms that need catalog-scale screening should evaluate IRCAM Amplify AI Music Detector (AIMD), which analyzes the audio waveform and supports API and SDK workflows.
No detector can universally prove that a vocal is human. A positive watermark or several consistent detector results can provide useful evidence; a “not detected” result only means that a particular tool found no signal within its coverage. That distinction is especially important for songs that combine a human production with one AI-generated or voice-converted vocal.
What you are actually trying to detect
“Is this song AI-generated?” can describe three different situations:
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- Fully synthetic music: the instrumental, arrangement and vocals were generated by a model.
- AI-assisted production: a person wrote or performed much of the song but used AI for arrangement, mixing, mastering, lyrics or another production task.
- Synthetic or transformed vocals: a human performance was converted into another voice, an AI singer supplied a layer, or a generated vocal was edited into an otherwise human recording.
Most published accuracy figures concern fully generated tracks, not the narrower question of whether one vocal layer is synthetic. A tool can therefore be correct about the material it is designed to recognize while still being unable to identify a short, heavily processed vocal passage in a hybrid song.
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Before scanning, write down the claim you need to test. “Was any AI used?” is broader than “Was the lead vocal generated?” and both differ from “Did this file pass through a particular provider?” The answer determines which evidence is relevant.
AI music detector comparison
| Tool | Best for | Input | Detection basis | Result and coverage | Access and scale |
|---|---|---|---|---|---|
| Deezer AI Music Detector | Listeners screening releases or playlists | Playlist from a supported streaming service | Signatures associated with fully AI-generated music | Vendor-reported 99.8% accuracy for fully AI-generated music; supports major generators such as Suno and Udio and adds others when examples are available | Free online tool; 27 languages; 20 common streaming platforms |
| IRCAM Amplify AI Music Detector (AIMD) | Labels, distributors, publishers, DSPs and collecting organizations | Audio tracks in batch or integrated workflows | Raw-waveform analysis rather than metadata or platform signals | Probability score of AI generation; supports Suno, Udio, Sonauto, ElevenLabs and other models | API/SDK and professional deployment; vendor claims more than 250,000 tracks per hour depending on server capacity |
| Google SynthID Detector | Provenance checks when a compatible Google watermark may exist | Supported audio file or Google verification flow | Imperceptible SynthID watermark | Positive result indicates compatible SynthID provenance; a negative result means only that no supported watermark was found | Public detector for supported files; broader commercial terms not stated |
| YouTube Music AI labels | Corroborating evidence on a platform release | Song listing in YouTube Music | Platform labels and detection systems used with music partners | Labels songs created primarily with generative AI; not a universal test for every transformation or mixed workflow | Rolling platform feature; availability can vary by release and region |
The figures in this table are vendor or platform claims, not an independent head-to-head benchmark for synthetic vocals. Deezer’s 99.8% number applies to fully AI-generated music. IRCAM’s 99% accuracy and less-than-1% false-positive figures are AIMD product claims. Neither number should be generalized to every voice-conversion or hybrid-production case.
Deezer AI Music Detector: the practical starting point for listeners
Deezer launched its free online detector on June 11, 2026. It is available in 27 languages and can scan playlists from 20 common streaming platforms, so you do not need the audio file in order to begin. Deezer says its system labeled more than 13.4 million detected tracks during 2025 and reports 99.8% accuracy for fully AI-generated music. Those are Deezer’s own published figures.
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The service is intended to recognize fully AI-generated music from major generators such as Suno and Udio. Deezer says it can add support for other systems when representative examples become available. A positive result is therefore strongest when the question is “Does this release resemble a fully generated track from a covered system?” It is not a definitive judgment that a particular singer’s isolated vocal was synthesized.
How to use it
- Open Deezer’s online AI Music Detector and choose the playlist-scanning option.
- Select the streaming service and playlist that contains the suspicious song.
- Run the scan and save the exact result, playlist or track name, and date. A dated record matters because detector coverage changes as new generators are added.
- For a song that receives no positive label, continue with provenance and credit checks instead of treating the result as proof of a human performance.
Playlist scanning is convenient, but it also means the detector is working from the platform version. A re-encoded stream, an edited upload or a later replacement master may not behave like the original file supplied by the creator.
Listener costs and practical limits
Deezer presents this detector as a free online service. The published information does not establish a paid tier, a per-track charge or a referral program, so do not promise a particular commercial arrangement. A playlist scan is also a screening aid, not a downloadable forensic report. If a result could affect a takedown, royalty dispute or public accusation, preserve the track and obtain a second, auditable review.
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IRCAM Amplify AIMD: professional catalog screening
IRCAM Amplify describes AIMD as a waveform-based detector: it analyzes the raw audio rather than relying on metadata, tags or a platform’s declaration. The product returns a probability score for AI generation and is designed for DSPs, distributors, labels, publishers and collective-management organizations. It lists support for Suno, Udio, Sonauto, ElevenLabs and other models.
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Why waveform analysis matters
Metadata can be removed or falsified, and a platform label may cover only the way a release was declared. An audio-based system can examine characteristics that survive when a file is moved between services. That does not make it infallible: compression, mastering, edits, unknown generators and a small AI vocal layer can all weaken the signal.
Published performance and throughput
IRCAM Amplify advertises 99% accuracy, less than 1% false positives and throughput above 250,000 tracks per hour depending on server capacity. These are vendor-published product figures, not an independent comparison with Deezer, SynthID or other detectors. Ask for the evaluation set, class definitions and handling of hybrid tracks before using a score as an enforcement threshold.
Questions to ask before buying or integrating
- Does the probability score refer to the whole mix, a segment, or a separate vocal stem?
- Which generator versions and voice-conversion systems are covered, and how quickly are new models added?
- What file formats, duration limits and batch sizes are supported?
- Can the API return a model version, confidence interval or explanation suitable for an audit?
- How are re-encoded, clipped, mastered and partially synthetic tracks evaluated?
- What retention, deletion and confidentiality terms apply to uploaded unreleased music?
When a professional workflow is justified
- A distributor needs to screen a large intake before delivery to stores.
- A label wants a repeatable review queue with probability scores and an audit trail.
- A publisher or collecting organization needs an API rather than manual playlist uploads.
- A platform wants to combine audio evidence with credits, declarations and account-level signals.
Commercial access and pricing are not stated here. Treat the service as a professional, gated workflow and confirm current terms directly with IRCAM Amplify. Throughput above 250,000 tracks per hour is also conditional on server capacity, not a guaranteed speed for every customer.
Google SynthID Detector: a provenance check, not a universal detector
Google says SynthID embeds an imperceptible watermark into audio generated or published through Lyria and NotebookLM. Its public detector can check supported audio files. If it reports a positive match, that is valuable evidence that the file passed through a compatible SynthID pipeline.
How to interpret “not detected”
A negative result means only that the detector did not find the watermark it knows about. The song may have come from an unsupported generator, may use a synthetic voice from another system, or may have been transformed in a way that prevents recognition. It does not establish that the vocalist was human.
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Use the original file when possible
Upload the earliest, highest-quality file you can lawfully access. Do not rely on a screen recording or a social-media transcode when an original master is available. Keep the tested file unchanged and record its filename, format and date so that another reviewer can reproduce the check.
Watermark verification answers a narrower question than waveform classification. It can establish compatible provenance when the mark is present, but it cannot estimate how much of a song was AI-made, identify a specific singer, or certify that an unmarked vocal was recorded by a person.
YouTube Music labels as corroborating evidence
YouTube Music is rolling out labels for songs created primarily with generative AI and says it uses detection tools with music partners. A label can support a detector result or a disclosure from the artist. It should not be treated as a forensic test for every vocal transformation, cloned voice or mixed human/AI workflow. Platform labels may also change as policies and detection coverage evolve.
Check the exact release and version: an official master, lyric video, remix and user upload may not share the same metadata or classification. Save a screenshot or URL with the date if the label matters to a dispute.
A step-by-step workflow for checking one suspicious song
1. Define the claim
Write down whether you are checking for a fully generated track, an AI voice, a converted human performance or any AI assistance. This prevents you from treating a tool designed for one category as an answer to another.
2. Run the accessible screen
If the song is in a supported streaming playlist, scan it with Deezer’s detector. Record the exact label, track version and date. Do not paraphrase a result later; wording such as “detected” and “not detected” has a narrower meaning than “human” or “fake.”
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3. Check compatible provenance
If Google-generated audio is plausible, submit the original file to SynthID Detector. A positive watermark result is stronger evidence than a negative result because it links the file to a supported provenance system.
4. Check platform and human documentation
- Read the credits for vocalists, producers and featured performers.
- Look for an artist or distributor AI-use disclosure.
- Check whether YouTube Music displays a generative-AI label.
- Compare the release version with official uploads; an unofficial edit may not match the credited master.
5. Escalate only when the decision warrants it
For a rights, payment or catalog decision, send the original audio through a professional service such as AIMD and preserve its probability score, model version and processing date. A consumer scan is useful triage, not a substitute for an auditable review.
6. Preserve evidence
Keep the original file, a hash if your organization uses one, screenshots or exported reports, the source URL, and the date of every check. Re-encoding, trimming, stem separation and mastering can change detector behavior, so test versions separately and label them clearly.
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Positive detector, no disclosure
Treat this as a reason for further review, not automatic proof of misconduct. Confirm that the tool covers the suspected generator, test the original master, and compare with credits and platform labels.
Negative detector, suspicious vocal
A negative result is weak evidence when the song is hybrid, heavily edited or made with an unknown system. A short vocal phrase may not contain enough signal for a model trained on complete synthetic tracks.
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First check whether they are answering the same question. SynthID searches for a specific watermark; Deezer and AIMD look for learned characteristics associated with generators; YouTube Music supplies a platform classification. Disagreement can reflect different coverage rather than a simple error.
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Why synthetic-vocal detection remains difficult
- Unknown generators: commercial detectors learn from particular models and training examples. A new or private system may leave a different signature.
- Editing and post-production: time stretching, denoising, equalization, re-recording and lossy encoding can alter the evidence.
- Hybrid songs: a human instrumental, AI backing vocal and converted lead can produce a result that does not fit a fully synthetic or fully human class.
- Small vocal sections: a detector may be evaluating the complete mix, where a short synthetic layer is masked by instruments and effects.
- Watermark scope: a watermark detector can only identify watermarks from the ecosystems it supports.
Current work on AI-audio robustness specifically treats edited audio and unknown-generator cases as difficult. That is why a detector score should be combined with provenance, credits and a reproducible file record.
Common mistakes to avoid
- Calling a “not detected” result proof that a singer is human.
- Applying Deezer’s fully generated-music accuracy figure to a single converted vocal.
- Comparing a streaming transcode with an original master and assuming they are equivalent test inputs.
- Using a platform label as if it were a laboratory finding.
- Publishing a probability score without naming the tool, date, file version and stated coverage.
- Uploading unreleased tracks to a service without checking retention and confidentiality terms.
Choosing the right tool
| Your situation | Best first action | What to add |
|---|---|---|
| You are a listener with a streaming link | Scan the playlist with Deezer | Check credits, disclosures and YouTube Music labels |
| You suspect audio made through Lyria or NotebookLM | Run the original file through SynthID Detector | Use a second detector if the decision has financial or rights consequences |
| You are screening a catalog | Evaluate AIMD’s batch/API workflow | Set review thresholds and retain scores, versions and files |
| You are investigating one transformed vocal | Preserve the original and identify the exact claim | Use multiple signals; do not rely on a fully generated-track accuracy figure |
What counts as strong evidence?
- Compatible watermark: a positive SynthID result directly supports provenance in that watermarking ecosystem.
- Convergent, covered detections: independent-looking tools agree, and the suspected generator is within their stated coverage.
- Documented disclosure: credits, artist statements or platform labels identify generative use.
- Single negative scan: useful as a lack of detected signal, but not evidence that the vocal is human.
For publication, moderation or royalty decisions, report the exact tool, version or date when available, input file, result wording and coverage. Say “the detector classified the track as likely AI-generated” rather than “the singer is not human” unless you have direct, documented provenance.
Frequently Asked Questions
Can any AI music detector prove that a vocal is human?
No. A negative result only means that the tested tool found no supported signal. Unsupported generators, edits, compression and short vocal layers can all produce false negatives.
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What is the best free AI music detector for listeners?
Deezer’s AI Music Detector is the most accessible starting point. It launched on June 11, 2026, scans playlists from 20 common streaming platforms, supports 27 languages and reports vendor-claimed 99.8% accuracy for fully AI-generated music.
Does Deezer detect an AI voice inside an otherwise human song?
Its stated scope focuses on fully AI-generated music from systems such as Suno and Udio. Do not treat a Deezer result as a definitive ruling on one synthetic or voice-converted vocal layer.
When should I use Google SynthID Detector?
Use it when the file may have been generated or published through a supported Google ecosystem such as Lyria or NotebookLM. A positive match is provenance evidence; “not detected” does not establish human authorship.
Is IRCAM Amplify AIMD free, and what does it cost?
The published product information does not state consumer pricing or a standard per-track fee. AIMD is positioned as a professional API/SDK service, so labels and platforms should request current commercial terms directly from IRCAM Amplify.
Why do two detectors disagree about the same song?
They may use different evidence and answer different questions. SynthID checks for a specific watermark, while Deezer and AIMD look for learned audio characteristics. Coverage, file versions, editing and hybrid production can all produce disagreement.
The Bottom Line
Use Deezer for an accessible first pass, SynthID for compatible watermark provenance, and IRCAM Amplify AIMD for professional catalog screening. Combine results with credits and platform disclosures, preserve the original audio, and never turn a negative detector result into a claim that the vocal is human.
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