MiniMax music detection
When a model copies the dynamics of a human reference, the features built on dynamics stop working.
- Output
- Complete songs from prompts or reference clips
- Distinctive feature
- Reference conditioning
- Detection angle
- Weakened dynamics features; spectral evidence only
- Attribution supported
- No
Upload Audio or Drag & Drop
Upload an audio file to receive a probabilistic analysis of characteristics associated with AI-generated music.
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MP3, WAV, FLAC, AAC, M4A, OGG, WebM · max 25 MB · min 10 seconds · 30+ seconds recommended
Your audio never leaves your device. Decoding and analysis run entirely in this browser tab, and nothing is uploaded to a server. Your audio is processed only to perform this analysis, and your uploaded audio and temporary analysis data are automatically deleted after processing. No report links are created, and your analysis is never publicly accessible. Upload only audio you are authorised to process — analysis does not transfer ownership or publishing rights.
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Inherited dynamics
Reference conditioning lets the model take the loudness envelope and general character of an existing recording. If the reference is a human track with natural crest factor, the generated output can inherit that profile — and crest factor is one of the cheapest, most commonly used detection features in the field.
This is a real weakness, not a hypothetical one. The engine reduces the weight of dynamics-derived evidence when other measurements disagree with it, and reports lower confidence rather than pretending the feature still holds.
What still carries signal
Spectral structure in the upper bands, stereo field movement over time, and cross-segment agreement are less affected by reference conditioning, because they depend on how the audio was synthesised rather than on how loud it is.
MiniMax detection FAQ
Because the features disagree. When dynamics point one way and spectral evidence points another, the honest output is a probability with a caveat attached, not a confident verdict.