How to Detect
AI-Generated Music

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.

Method 1: Listening cues (fast, subjective)

Before reaching for any tool, experienced ears can often spot AI-generated music by the following patterns:

• Metallic sheen on sustained notes. Held chords, pads, and long tones often carry a subtle shimmer — a spectral smear that doesn't exist in recorded acoustic instruments. Most audible in the 4–12 kHz range on full mixes.
• Watery or swirling modulation. Rapid micro-variations in pitch and timbre on sustained sounds. This is phase inconsistency from the codec's overlap-add reconstruction — it sounds like mild chorus or subtle tape flutter, but it's not musical.
• Sections that feel "glued" in a non-organic way. AI generators assemble music token-by-token at a global context level. Transitions and tension builds often feel smooth in an uncanny way — correct in structure but missing the physical momentum of human performance.
• Vocals that float above the mix. AI-generated vocals typically don't interact with room acoustics the way live recordings do. They sit in the mix with perfect phase alignment that sounds sterile rather than present.
• Dynamics that compress too early. AI generators implicitly target streaming loudness targets during generation. The dynamic arc — verse quiet, chorus loud — is often exaggerated and arrives slightly ahead of where a human arrangement would place it.

Method 2: Spectral analysis (intermediate)

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.

What to look for in a spectrogram

• Horizontal striping above 8–10 kHz on sustained sections. The stripes repeat at intervals corresponding to the codec's frame size (~23 ms for most EnCodec variants).
• Unusual harmonic regularity in noise floors. Real recordings have stochastic high-frequency noise; AI-generated tracks often have quasi-periodic structure in the same region.
• Hard spectral ceiling. Many generators produce audio that abruptly rolls off above 11–14 kHz — the bandwidth limit of the codec's learned spectral model. Real recordings (especially at 44.1 kHz) have continuous, irregular energy to 20 kHz.

Tools for spectrogram analysis

• iZotope RX — spectral repair suite with high-resolution spectrogram display
• Sonic Visualiser (free) — open-source waveform and spectrogram viewer
• Adobe Audition — Spectral Frequency Display mode
• Any DAW with a spectrum analyzer plugin (e.g., SPAN by Voxengo, free)

Method 3: Automated forensic detection (most reliable)

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	0.903	8 generators	Research paper only
CLAM	0.871	6 generators	Research paper only
Listening alone	~0.65	Varies by experience	Free

How ArtifactNet detects AI music

ArtifactNet uses a three-stage forensic pipeline:

1. ArtifactUNet (3.6M parameters) — extracts the codec residual from the magnitude spectrogram using a bounded-mask UNet. The residual is the subtle difference between what the codec encoded and what it reconstructed.
2. 7-channel HPSS forensic features — decomposes the residual into harmonic and percussive components via Harmonic-Percussive Source Separation (HPSS). AI-generated audio has a characteristic ratio of harmonic to percussive residual energy that differs from real recordings.
3. Lightweight CNN (0.4M parameters) — processes the track in 4-second segments, then aggregates a song-level verdict with a confidence score.

Total model size: 4.2M parameters. Inference takes 5–10 seconds on GPU for a 4-minute track. False positive rate on real music: 1.49% — meaning 98.51% of genuine human recordings are correctly identified as non-AI.

Detection after processing: does mastering fool the detector?

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.

Use cases for AI music detection

• Music distributors and labels — screening catalog submissions for undisclosed AI-generated content before signing or releasing.
• Sync licensing platforms — verifying that tracks submitted for TV, film, or advertising are human-authored as represented.
• Streaming platforms — enforcing policies around AI-generated content metadata disclosure.
• Producers and engineers — understanding what's in a delivered session before committing to a mix or master.
• Copyright and legal — supporting authorship claims in disputes where AI generation is contested.

The de-artifact API provides batch processing for catalog-scale detection — submit 100+ tracks at once and receive forensic verdicts with confidence scores via REST API or web dashboard.