The AI music revolution is moving fast. Platforms like Suno, Udio, and Boomy are now generating over 100,000 tracks per day. Some are obvious “slop” — but others are terrifyingly convincing.
As a music professional or even just a dedicated listener, being unable to tell the difference is no longer an option. Whether you’re a playlist curator trying to keep your selection human, or a label A&R vetting a new submission, you need a toolkit for spotting fakes.
I’ve analyzed thousands of AI-generated tracks over the last few months. Here are the 7 most reliable methods I use to identify them — ranging from simple listening tips to forensic analysis.
1. The “Vocal Glue” Effect (Listen for Smearing)
The first giveaway is almost always in the vocals. AI models generate audio as a single stream of data; they don’t record a voice and “mix” it in. This often results in a strange “smearing” effect where the vocals bleed into the instrumental backing.
What to listen for:
- Plosives (P, B, T): Real recordings have distinct bursts of air. AI often smooths these out.
- Vocal Fry: AI struggles with the natural “gravel” or fry in a human voice, often replacing it with a weird digital buzz.
- Breathing: This is the big one. AI singers either never breathe (singing impossible 30-second phrases) or breathe in weird, unmusical places.
2. The High-Frequency “Sand”
Current generative models (like Suno v3 and even v4) struggle to reconstruct high-frequency information cleanly. This leaves a telltale artifact that audio engineers call “sand” or “grit.”
If you listen to the track on high-quality headphones, focus on the 10kHz+ range. You might hear a constant, low-level static that sounds like a low-quality MP3, even if the file is a WAV. This “hazy” top end is a signature of diffusion models.
3. The 8-Bar Loop Trap
AI music generators are trained on massive datasets of pop music, which makes them obsessed with structure — specifically, 8-bar loops.
While human music is repetitive, it’s usually progressively repetitive. A human drummer hits the hi-hat slightly harder in the second chorus. A guitarist changes the strumming pattern slightly.
The AI Tell: If the song feels like it’s strictly adhering to a rigid grid with zero variation in velocity or timing for 3 minutes straight, be suspicious. AI often loops the exact same audio waveform for the chorus, whereas a human recording would have subtle differences in each take.
4. Metadata Forensics
Sometimes the easiest way to catch an AI track isn’t to listen to it — it’s to look at the file header.
When you download a track directly from a generator, it often keeps the platform’s metadata tags unless the uploader explicitly strips them.
How to check:
- Use a tool like ExifTool or MediaInfo.
- Look at the
EncoderorWriting Librarytag. - Common giveaways:
Lavf(standard FFmpeg encoder often used by these tools),Suno, orUdio.
5. Spectrogram Analysis (Visual Fingerprinting)
If your ears are deceiving you, your eyes won’t. AI music leaves distinct visual patterns in a spectrogram.
The “Hard Cut”: Many models have a hard frequency cutoff. For example, some older models chop everything off above 16kHz. If you see a perfectly flat line at 16kHz or 20kHz where the audio just stops, that’s a red flag (or a sign of bad MP3 transcoding).
The “Grid”: Suno tracks often exhibit a faint “grid-like” texture in the spectrogram, especially in the 5-10kHz range, caused by the tokenization process of the model.
6. Impossible Instrumentation
AI doesn’t know how instruments are played; it just knows what they sound like. This leads to physical impossibilities.
Examples I’ve heard:
- A guitar playing a chord with 7 notes (when guitars have 6 strings).
- A drummer hitting a crash cymbal, snare, and two toms simultaneously (unless they have four arms).
- A flute taking a breath while playing a continuous note.
7. Use a Dedicated AI Detector
Finally, you can use specialized tools trained to spot these artifacts.
We built the AI Music Detector specifically for this. It uses a hybrid approach — analyzing both the audio features (like the “sand” and vocal anomalies) and the file structure to give you a probability score.
It’s free to use and works on Suno, Udio, and Boomy tracks.
Conclusion
As we move further into 2026, detecting AI music is going to get harder. Models are improving every week. But for now, these models are imperfect. They leave traces.
Trust your gut. If a track feels “soulless,” “hazy,” or just “too perfect,” run through this checklist. There’s a good chance you’re listening to a ghost.