AI music generators — Suno, Udio, Stable Audio, MusicGen — produce tracks that sound increasingly polished. Yet they leave behind a forensic signature: residual artifacts baked into the audio by the neural codec at the heart of every modern generator. This guide explains how to detect them, from quick listening cues to automated forensic tools.
Want the fastest check? Skip straight to the automated tool — run any track through ArtifactNet free (no install, no account) and get a forensic verdict in seconds.
Before reaching for any tool, experienced ears can often spot AI-generated music by the following patterns:
Limitation: Listening cues become unreliable as generators improve. Suno v4 and Udio's latest models produce outputs that fool the ear in casual listening. For reliable detection — especially in professional or legal contexts — use a forensic tool.
In a spectrogram, RVQ codec residuals appear as faint, structured patterns above 8 kHz that are spectrally regular — unlike the stochastic texture of recorded music or noise. This is visible in any DAW or audio analysis tool that can display a high-resolution spectrogram.
Manual methods require expertise and time. Automated tools use machine learning to detect the same codec residuals, without requiring prior listening experience.
| Tool | F1 score | Generators covered | Access |
|---|---|---|---|
| ArtifactNet (Intrect) | 0.9829 | 22 generators incl. Suno, Udio, Stable Audio, MusicGen | Free demo, API (Pro plan) |
| SpecTTTra | 0.7713 | 8 generators | Research paper only (arXiv:2604.16254v2, Table V) |
| CLAM | 0.7576 | 6 generators | Research paper only (arXiv:2604.16254v2, Table V) |
| Listening alone | ~0.65 | Varies by experience | Free |
ArtifactNet's key advantage: it targets residual physics — the mathematical structure of RVQ quantization error — rather than generator-specific fingerprints. In the paper's held-out generator evaluation it correctly identifies music from generators it was never trained on. This is measured on the generators tested in arXiv:2604.16254v2, not a guarantee for every future generator — even ones sharing the same codec architecture.
ArtifactNet uses a three-stage forensic pipeline:
Total model size: 4.0M parameters (ArtifactUNet 3.6M + CNN 0.4M). Inference takes 5–10 seconds on GPU for a 4-minute track. False positive rate on real music (ArtifactBench, v9.4, unseen test): 1.49% — meaning 98.51% of genuine human recordings are correctly identified as non-AI.
Upload any track. No account required.
A common question from label A&R teams and distributors: can an AI-generated track evade detection if it's been mastered, EQ'd, or processed through analog hardware?
Short answer: no, for current techniques. RVQ codec residuals are spectral patterns that survive standard dynamic processing. EQ can shift which frequencies carry the residual energy, but it doesn't destroy the underlying quantization structure. ArtifactNet's residual extractor is designed to find this signature even when the track has been through a typical mastering chain.
The one exception: if the AI-generated audio is re-recorded through an analog signal path (e.g., played through speakers and recorded with a microphone), the recording process adds room acoustics and analog noise that can partially mask the codec residual. Even so, ArtifactNet retains statistically significant detection accuracy on such re-recordings in the paper's tests (arXiv:2604.16254v2); this is measured on those tested conditions, not a guarantee for every recording setup.
The ArtifactNet Cloud provides batch processing for catalog-scale detection — submit many tracks at once and receive forensic verdicts with confidence scores via REST API or web dashboard.