What your result actually means

The one-paragraph version

The number is not a verdict and it is not a confidence score. It is an estimate of how much your audio resembles patterns that are common in AI-generated music. High means “this looks like a lot of generated tracks we can measure”, not “this was made by Suno”. Nothing in the result identifies a specific generator, and nothing proves authorship either way.

The percentage

The headline figure is an AI-likeness estimate between 0 and 100. It is deliberately capped and pulled toward the middle, so you will rarely see extremes: an uncalibrated detector that hands out 99% is telling you about its own confidence theatre, not about your song. The range shown next to it (for example 45–75%) matters more than the single number, because the true value could sit anywhere inside it.

The four labels

Likely AI-generated (70% and above)

Several independent measurements pointed the same way. This is the strongest signal the tool gives, and it is still evidence, not proof. Treat it as a reason to ask questions about a track, never as grounds to accuse someone.

Possibly AI-generated (58–69%)

Some measurements leaned toward generated audio and others did not. Very common with heavily produced, quantised or loudness-maximised human music, which shares a lot of surface behaviour with generated audio.

Likely human-created (below 58%)

The measurements look like a recording of performances: irregular timing, natural noise floor, a stereo field that moves. Note this is also what a well-produced AI track can look like once someone has re-recorded, re-mixed or re-mastered it.

Inconclusive

Not a failure. The tool returns this when the audio is too short or too compressed to read, when the analysed sections disagree with each other, or when the evidence sits right on the boundary. A detector that never says “I do not know” is hiding its errors from you. A longer, higher-quality upload usually resolves it.

Confidence: Low or Moderate

Separate from the percentage, the report tells you how much weight the analysis itself deserves. Moderate means the file was long enough and clean enough for the measurements to be meaningful. Low means something limited the analysis, usually a short clip or lossy encoding. There is no “High” setting, because no published evaluation currently justifies one. See accuracy and limitations for why.

Why the result explains itself

Every report includes a “how this estimate was reached” section listing the actual measurements behind it: spectral roll-off and high-frequency behaviour, stereo field width and correlation, dynamic range and crest factor, timing regularity, and noise-floor character. If you disagree with the score, you can see exactly which measurement drove it rather than arguing with a black box.

What can push the result the wrong way

• Low-bitrate MP3 or streaming rips. Lossy encoding strips the high-frequency detail the analysis leans on, which nudges scores upward.
• Heavy limiting. A modern loud master flattens dynamics in the same way generated audio often does.
• Grid-locked production. Fully quantised electronic music looks machine-regular because it is.
• Post-processing on generated tracks. Re-recording, adding live takes or re-mastering an AI track can move it back down.
• Short clips. Under about 20 seconds there is not enough material to read.

What this result is not

• Not proof for a copyright claim, a dispute or a takedown.
• Not an identification of which generator was used.
• Not a check for plagiarism, sampling or voice cloning of a specific artist.
• Not a platform decision: Spotify, YouTube and distributors run their own systems.

Privacy, since people ask

The analysis runs entirely in your browser. Your audio is never uploaded to a server, there is no account or sign-up, results are held in the page for the session only, and the file is released from memory as soon as the analysis finishes. Close the tab and nothing is left.

Try it on your own track

Run a file through the free detector, then come back to this page to read the output. If you want the mechanics rather than the interpretation, see how it works and the methodology.