How to tell if a song is AI-generated
In July 2026, Deezer reported that fully AI-generated tracks had peaked at more than half of its new-music uploads - roughly 90,000 tracks a day, up from about 10% of daily uploads in early 2025. Knowing how to spot AI music has gone from trivia to a daily skill, whether you're a playlist curator, a blogger, a teacher, or a listener who just noticed a suspicious "new artist."
If you're searching for how to tell if a song is AI generated, here's the honest version you won't get from detector-tool marketing pages: no single test proves a song is AI-made. The workable approach combines four methods, ranked below by how much weight to give them. And a disclosure that works in your favor: we build AI music tools at RaoMusic, so this guide is written from the inside - we know exactly what generated tracks sound like, because we make them.
The quick answer: check the artist's background first (no history = biggest red flag), then listen for vocal and structure tells, use a free spectrogram if you're technical, and treat detector scores as evidence, never proof.
To check if a song is AI-generated:
Research the artist (socials, live shows, credits, output pace)
Listen for vocal artifacts (foggy tone, no breaths, smeared consonants)
Check structure and lyrics (rigid grid, copy-paste chorus, generator-word tics)
Inspect a spectrogram if you're technical
Run an AI song detector as a final, non-conclusive signal
Method 1: Check the artist's background (the most practical first check)
News coverage of viral mystery acts - including The Velvet Sundown, the band whose music news reports traced to AI generation - leaned on background checks, not audio analysis, to raise the questions. AI can fake a song easily; faking a career is much harder. For most listeners, this is where to start.
Work through this checklist:
Social media history. Real artists have years of posts, tagged photos, interactions. AI-farmed artists have three tracks and a stock avatar, or a profile created last month.
Live performance trail. Gig listings, ticket history, concert footage on YouTube, tagged venue photos. AI artists don't tour.
Credits and collaborators. Check songwriting/production credits on the track, and whether other real people acknowledge working with them.
Unrealistic productivity. Media researchers have pointed to the BBC that AI-farmed catalogs show unrealistic levels of productivity - multiple full albums of soundalike material appearing within weeks, at a pace no human writer maintains.
Chart math. Reporting on fake-artist networks flags repeated chart entries with low actual sales, and popularity curves that grow too fast and too uniformly to be organic.
Check the generators themselves. If you suspect a song came from Suno or Udio, search the track or artist name on those platforms - creators often (not always) post their generations there, sometimes under the same name. You'll need an account, and it works best when you already have a name to search.
A song with no artist history, no live trail, and a too-clean catalog isn't confirmed AI - but it's the strongest signal you'll get without audio forensics.
Method 2: Listen for the tells (what AI still gets wrong)
The models improved fast - the old "AI vocals sound drowned in reverb" tell is dead. But generated songs still leave fingerprints. These are the cues working musicians and AI practitioners (including us) actually use:
Vocals - the #1 giveaway
"Foggy" vocal quality. The vocal sits slightly smeared and over-compressed while the instruments sound normal - a mismatch human mixing rarely produces.
Smeared consonants and off pronunciation. Fast lyrics blur at the consonants; odd word emphasis that a human singer wouldn't choose.
No breaths or mouth sounds. Listen between phrases: real singers breathe, swallow, and click. Generated vocalists often don't - the silence is too clean.
Mechanically consistent vibrato and pitch. Perfectly steady vibrato on every held note, pitch that never wavers microtonally the way a human voice does.
Timbre that drifts mid-phrase. The voice subtly changes character within a line - a model artifact, not a vocal choice.
Emotion that doesn't match the words. Heartbreaking lyrics delivered with the same pleasant, flat affect as everything else.
Lyrics
Generator tics. Suno users report the model leans hard on "neon," "shadows," and "whispers" - one user called it a dead giveaway to the AP. Every model has favorite words; hear enough generated songs and you collect the list.
Verses you could swap between songs. If verse two of song A works untouched as verse one of song B, no one was telling a story.
Rhymes that scan but don't add up. Rhyme schemes stay tidy while the narrative makes no actual sense.
Structure and performance
A rigid 8/16/32-bar grid. Everything in tidy blocks, copy-pasted choruses, drum loops whose energy never shifts for the whole song.
Flat dynamics. Ballads that never build, climaxes that arrive unearned, drums with no velocity variation - machine-perfect timing that stands out especially in folk, jazz, and soul.
Parts that don't interact. The bass doesn't lock with the kick, backing vocals feel unrelated to the lead - because each element wasn't performed in a room with the others.
Mix and mastering
Phasey, washy high end. Hi-hats and cymbals with a "sizzle" that sounds like sand rather than metal - spectral smearing where detail should be crisp.
Overcompression and distortion on demand. Overdriven 808s and a generally loud, small sound.
Suspiciously uniform stereo. A perfectly consistent stereo image across the whole track - real mixes wander a little.
No single cue convicts a song. A folk demo with rigid timing might just be a DAW quantized by a human. Autotuned vocals fake several "AI" cues in reverse. Stack several cues before concluding, and hold your conclusion loosely.
One bridge before the technical methods: if you *make* AI music, read this list as a quality-control checklist rather than a threat - every tell is a mixing decision away from gone.
Method 3: Look at the spectrogram (for the technical)
A spectrogram shows the song's frequencies over time, and it's free: open any audio file in Audacity and switch to spectrogram view.
What generated audio tends to show:
Missing or thin harmonic overtones - the fundamental frequencies are there but the natural overtone series above them looks sparse or artificial
High-frequency smearing - energy above ~10 kHz that's a fuzzy wash instead of distinct harmonics
Unnaturally clean gaps - frequency regions that are eerily silent, with no room noise or performance bleed
Fair warning: this method needs a trained eye. It's most useful for confirming a suspicion, not forming one - and heavy mastering can scrub or fake what you're looking for.
AI song detector tools: what they can and can't tell you
An AI song detector is the most-marketed method and the easiest to overinterpret. Every accuracy number below is self-reported by the vendor with no independent verification, and an AP reporter's 2025 hands-on test found that detectors accepting streaming links often returned inconclusive results, or flagged AI songs as human and vice versa. That same test showed the upside: an Ircam Amplify scan scored the reporter's AI-generated uploads at 81.8-98% probability and correctly attributed them to Suno, while old human-made MP3s scored very low. Use detectors as one input, not a verdict.
| Tool | Input | What it claims | Best for | Catch |
|---|---|---|---|---|
| SubmitHub AI Song Checker | Link or file upload | "Mostly accurate" | Quick free first pass | Vague accuracy claim; the most-cited casual tool |
| Deezer AI Music Detector | Scans up to 100 of your playlists (login) | 99.8% accuracy, tagged 13.4M AI tracks in 2025 | Checking what's already in *your* libraries | Only scans playlists you own; not a per-song verdict tool |
| AHA Music detector (ACRCloud) | File or link | Separate scores for mix/vocals/accompaniment; attributes the generator (Suno, Udio, ElevenLabs...) | Finding out *which* tool made it | 5 free checks/day; admits post-processing degrades accuracy |
| authio | File upload | 99.42% accuracy (ensemble of models) | Second opinion on an upload | No methodology published for the claim |
| Ircam Amplify AIMD | File upload | 99% accuracy, <1% false positives | Professional/industry use | Built for labels and platforms, not casual listeners |
The honest limits of all of them:
They can be fooled. Re-recording, mastering, pitching, or low-bitrate compression destroys many detectable artifacts.
They false-positive on humans. Heavily Autotuned or vocoded human vocals trigger AI-vocal flags; rigidly quantized EDM triggers structure flags.
Hybrids sit in a gray zone. Human-written lyrics over an AI instrumental, or an AI vocal in a human production - most tools can't split these, and honest ones score them in the middle.
It's an arms race. Detectors train on the previous model generation; generators update constantly. Any "tell" in this article has a shelf life, including ours.
A note on responsibility: this is practical guidance, not forensic or legal advice. Don't publicly accuse an artist, label, or distributor of fraud based on a detector score or your own ear - even Deezer, running the most accurate detector in production, treats its output as tagging input, not verdict.
What streaming platforms are doing about it
For listeners, the practical signal is growing: Deezer labels detected AI tracks "AI-generated content" in the app (launched June 2025) and excludes fully-AI tracks from algorithmic and editorial recommendations - which is also why fully-AI music still accounts for only 1-3% of actual streams on Deezer despite dominating uploads. An Ipsos survey of 9,000 listeners across 8 countries (commissioned by Deezer, November 2025) found 97% of fans can't reliably distinguish AI songs from human ones by ear, 80% want AI music labeled, and 52% oppose AI songs appearing in the main charts.
Spotify and YouTube don't yet show listeners a blanket AI label; instead, many distributors ask uploaders to declare AI-generated content at upload - DistroKid, TuneCore, and CD Baby all include AI-disclosure steps - and false or misleading declarations can create takedown and account risks under platform rules. YouTube has also built synthetic-singing detection into its Content ID system for rights holders.
If you're making AI music: how not to sound like the fakes
Here's the constructive flip side - and why we're comfortable writing this guide. The tells above describe lazy AI music: default prompts, no editing, mass uploads. Nothing forces your AI-made songs to have those tells:
Edit the structure. Cut the copy-pasted chorus, vary the dynamics, break the 8/16/32 grid. Two hours in any editor removes the biggest structural tells.
Write real lyrics. Generator tics mostly come from generator-written lyrics. Bring your own words (or use a lyrics tool deliberately, then edit hard) and the "neon/shadows/whispers" problem disappears.
Go hybrid. AI instrumental + your recorded vocal, or AI demo + human re-performance - hybrid workflows are where AI music is actually good, and they're also the honest middle ground for disclosure.
Respect the disclosure rules. Declare AI content at your distributor, label it where platforms ask, and don't mass-upload soundalike catalogs - that's the behavior getting accounts terminated, not AI itself.
Try it with your own material: RaoMusic's AI music generator is free to start, and the same tools that generate songs also separate stems, convert audio to MIDI, and build covers - everything you need to take a generation from raw output to something that sounds deliberate.
Frequently Asked Questions
Combine four checks, in order of reliability: the artist's background (socials, live shows, credits, upload pace), listening cues (foggy vocals, no breaths, rigid structure, generator-word tics), a spectrogram if you're technical, and a detector tool as a final, non-conclusive input. No single test proves it.
Vendor claims range from "mostly accurate" to 99.8%, but every number is self-reported with no independent verification - an AP test found link-based detectors often inconclusive or wrong. Mastering, pitching, and compression all reduce accuracy, and heavily processed human vocals cause false positives. Treat detector scores as evidence, not proof.
Partially. Strong cues include smeared consonants, no breaths between phrases, mechanically steady vibrato, flat dynamics, copy-pasted choruses, and generator lyric tics like "neon," "shadows," and "whispers." But Deezer's survey found 97% of listeners can't reliably distinguish AI from human songs - listening alone is the weakest method on its own.
Deezer does - detected AI tracks show an "AI-generated content" label and are excluded from recommendations. Spotify and YouTube don't yet show listeners a general AI label; disclosure is enforced at the distributor level (DistroKid, TuneCore, CD Baby require you to declare it), and YouTube detects synthetic singing for rights holders via Content ID.
Per Deezer's reporting: about 10% of daily uploads in early 2025, roughly a third by late 2025, and a peak above 50% by mid-2026 - around 90,000 AI tracks per day. Deezer tagged 13.4 million AI tracks across 2025, and deemed up to 85% of streams on fully-AI tracks fraudulent. Yet fully-AI music makes up only 1-3% of actual listening on Deezer, because it's excluded from recommendations.
ACRCloud's detector (via AHA Music) attributes tracks to specific generators including Suno, Udio, ElevenLabs, and others; Pex/Vobile and Ircam Amplify offer generator attribution for industry clients. Attribution claims are strongest on raw, unmastered exports and weaken after heavy processing.
No. Detector output isn't accepted as proof the way fingerprint matches are - scores are probabilistic, vendors publish no independent methodology, and both false positives and false negatives are documented. For disputes, background evidence (or its absence) carries more weight than any detector score.
Yes - AI music itself isn't banned, but the rules around it are strict: declare it at your distributor, don't fake streams, don't pass it off with fake artist personas, and don't imitate real artists' voices without consent. Violations (not AI itself) are what get music removed and accounts terminated.
Usually easier to spot than fully generated songs, because the instrumental is the original master: Content ID can match the underlying recording even with a new vocal. Voice-converted vocals also carry conversion artifacts (timbre drift, smeared sibilants) that trained detectors flag.
Edit the generated structure, write or heavily edit the lyrics, consider a hybrid workflow (AI instrumental + your vocals), and follow disclosure rules. The tells in this article describe default outputs - deliberate post-production removes most of them. RaoMusic's generator plus its stem separator and MIDI editor exist exactly for that cleanup pass.