Does AI mastering make my human track look AI-generated?

It can push it in that direction. Automated mastering flattens several of the same statistics that generation flattens. If you know a track was AI-mastered, factor that into how you read the result.

Can this tool tell me which generator made a track?

No. The engine estimates how likely it is that a recording was machine-generated. It does not attribute output to a named platform, because attribution needs a classifier trained on labelled per-generator data and we have not published one.

Is the check free and private?

Yes. Analysis runs in your browser, the file is never uploaded to a server, and the result exists only for the current session.

Mureka (Sonauto) detection

A style-transfer pass rewrites the surface of a track, and surfaces are what spectral measurement reads.

Also known as: Sonauto
Output: Prompt-to-track with vocal control
Detection angle: Spectral-centroid variability
Attribution supported: No

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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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Smoothed brightness

Live performance produces constant small changes in brightness: a pick attack, a cymbal, a consonant, a fret buzz. Measured over time, spectral centroid in a human recording jitters. A style-transfer pass tends to regularise that jitter, producing a flatter brightness curve than the same arrangement played by people.

What else flattens brightness

Multiband compression, aggressive limiting, spectral-balance plug-ins and AI mastering services all reduce brightness variability in human recordings. Any of them can push a genuine performance towards the same reading, which is why this feature is weighted moderately rather than decisively.