Guides and technical articles on AI music forensics, artifact removal, and production.
A stem pulled out of a mix carries two different problems — residue smeared through the signal you kept, and bleed from the instruments around it. How to tell which one you have, and why the order you treat them in matters.
Gates cut the tail and the body with it; EQ notches dull the stick. The ring is harmonic and the stick is percussive — separate them and you can attenuate one without touching the other.
A room resonance only blooms on certain notes, but a static notch cuts that frequency for the whole take. Why a dynamic EQ band gets closer, and what per-bin suppression does differently.
What the metallic shimmer and watery sustains in Suno tracks actually are, why the free plan's MP3 export makes them worse, and which preset to reach for depending on your export format.
Closing the genre gap and the false-positive rate we promised to fix — a ground-up retrain on 27,476 tracks, a borderline-call review model, and the deployment bug we caught mid-rollout.
Negative results, evaluation leakage, and what we measured wrong — three failed experiments, a data-leakage discovery, and the real weakness hiding behind an illusory one.
Distillation and runtime — why light doesn't mean fast. Two parameter-count inversions, a causal real-time UNet, and a 50x speedup from a single ONNX surgery.
Listening cues, spectrogram analysis, and automated forensic tools for identifying AI-generated music from Suno, Udio, Stable Audio, and more. Includes benchmark comparison.
Step-by-step guide to removing the metallic, watery, swirly artifacts from AI-generated music. Covers RVQ ghosting, codec residue, and HF aliasing — what they are and how to fix them.
Chasing SONICS scores while real-world performance collapses — how a benchmark-saturated model went from SOTA to a 4.3% detection rate on the newest generator, and the fair-comparison harness we built in response.