Can this evidence prove a track was AI-generated?

No. It can support a review, but origin and authorship require provenance and context. Do not use one automated or acoustic observation as proof.

What should I do when the evidence conflicts?

Preserve the disagreement, inspect file quality and production context, and request documentation if the decision matters. Inconclusive is a valid outcome.

Comparison / Field notes

Detector or Trained Ear? Give Each One the Right Job

Use each observer for the work it can do.

4 August 2026 · 11 min read

Two complementary strengths

Automated classification applies one system consistently at scale. A listener understands lyrics, genre, performance intent and context. The machine lacks biography; the listener brings bias and fatigue.

A reconciliation workflow

Listen and record observations before seeing the report. Compare the verdict, component indicators and timeline with those notes. Investigate disagreements instead of averaging them, then obtain provenance when the outcome matters.

Context changes the balance

At catalogue scale, automation can prioritise review. In an individual dispute, documentary evidence dominates. Electronic genres, processed vocals and unfamiliar musical traditions increase the risk of confident but misplaced human intuition.

Improve the review process

Use known-origin controls, blind listening and written rubrics. Track corrections over time. The goal is calibrated uncertainty, not learning a list of supposedly universal AI tells.

Key takeaway

The short version

Machines offer consistency; people supply context. Neither supplies proof, and the strongest workflow keeps their observations separate until review.

Run a free detection

Frequently asked questions

No. It can support a review, but origin and authorship require provenance and context. Do not use one automated or acoustic observation as proof.