Songprint is a free online tool that detects whether a piece of music was generated by an AI model or performed by a human. Upload an audio file and receive a verdict with a confidence score.
The free tier allows 3 detections per day without registration. Pro adds generator attribution (which AI model most likely produced the audio), detection history, batch checks and exportable reports.
AI-generated vs human verdict with confidence score. 3 free detections per day with no signup. Generator attribution on Pro shows which AI model made the audio. Detection history, batch checks and exportable reports.
Labels and publishers screening demo submissions for AI-generated material. Music supervisors verifying track provenance before licensing. Creators checking whether a licensed track was secretly AI-generated.

A free detector that returns a confidence score, with 3 checks a day and no signup, is useful as a gut-check before a track goes into a playlist or a client deliverable. The hard part of AI-music detection is short clips and heavily compressed uploads, so I am curious what minimum duration and bitrate you treat as reliable, and whether the score drops when the file is an MP3 re-encode of something that started as WAV. Generator attribution on Pro, naming which model likely made the audio, is the part I would actually pay for, because a bare AI-versus-human label is not enough when someone asks which generator. Batch checks and exportable reports matter more for a catalog than for one-off files.

I build audio tools and kept seeing AI-generated tracks passed off as human work in demo submissions and playlists. Existing detectors are either buried behind paywalls or built for labs, not for someone who just needs a quick verdict. Songprint is my answer: paste a link or drop a file, get a confidence score in seconds. The first 3 checks each day are free, no account needed.
I like that the free tier needs no signup and still gives a confidence score rather than just a yes/no verdict. The use case of labels screening demo submissions makes a lot of sense. How does it handle tracks that are partly AI, like a human vocal over an AI-generated backing track? A note on how to interpret mid-range scores would be helpful.



A free detector that returns a confidence score, with 3 checks a day and no signup, is useful as a gut-check before a track goes into a playlist or a client deliverable. The hard part of AI-music detection is short clips and heavily compressed uploads, so I am curious what minimum duration and bitrate you treat as reliable, and whether the score drops when the file is an MP3 re-encode of something that started as WAV. Generator attribution on Pro, naming which model likely made the audio, is the part I would actually pay for, because a bare AI-versus-human label is not enough when someone asks which generator. Batch checks and exportable reports matter more for a catalog than for one-off files.

I build audio tools and kept seeing AI-generated tracks passed off as human work in demo submissions and playlists. Existing detectors are either buried behind paywalls or built for labs, not for someone who just needs a quick verdict. Songprint is my answer: paste a link or drop a file, get a confidence score in seconds. The first 3 checks each day are free, no account needed.
I like that the free tier needs no signup and still gives a confidence score rather than just a yes/no verdict. The use case of labels screening demo submissions makes a lot of sense. How does it handle tracks that are partly AI, like a human vocal over an AI-generated backing track? A note on how to interpret mid-range scores would be helpful.
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