Read the signal
Explore the acoustic patterns associated with synthetic music, with an emphasis on evidence over assumptions.
AI music intelligence
Listen beyond
the obvious.
A closer look at the signals behind a song. An evidence-minded approach to AI music detection for artists, labels, and the people who protect their work.
Closed alpha release · Invitation only
Sound has a story.Look a little closer.
A clearer perspectiveAudio signalsTrack contextMeasured uncertainty
The product perspective
More understanding.
Less guesswork.
Music is changing. The questions we ask about its origins deserve more than a quick yes or no.
Keep the context
Approach a recording as a whole, while considering how different sections and production choices shape the picture.
Respect uncertainty
A useful assessment makes room for ambiguity. Confidence, limitations, and an inconclusive outcome belong in the conversation.
Thoughtful by design
An assessment.
Not an accusation.
TimbreSift is for people asking careful questions about musical origins. Our product focus is clear: useful audio evidence, understandable findings, and responsible interpretation.
AI detection and music matching answer different questions. For recording fingerprints and comparisons between tracks, explore MotifSpan.
Independent artistsLabels & distributorsMusic researchers
01
Start with the right question
Establish whether the concern is synthetic generation, mixed human and AI contributions, or a connection to another recording.
02
Consider more than one signal
Evaluate the recording through complementary perspectives rather than letting a single score stand on its own.
03
Make the limits visible
Keep model coverage and uncertainty alongside the findings so the result can support an informed decision.
A little more clarity
Good questions.
Clear answers.
Is TimbreSift publicly available?
TimbreSift is in closed alpha. Access is invitation-only, with requests reviewed as places become available. Public, self-service scanning is not open at this time.
Does an AI score prove how a song was made?
No. An assessment can identify signals associated with synthetic audio, but it cannot establish every part of a song’s production history. Mixed workflows and unfamiliar generators require particular care.
Can it identify the music used to train a generator?
An AI-detection result cannot identify a generator’s training catalog. Comparing recordings and musical passages is a separate task, addressed by our companion product, MotifSpan.
Where do I submit a track?
Track submissions are arranged separately for invited alpha participants. This public website accepts access requests, not audio uploads.
Closed alpha release
Be part of a
closer listen.
Bring a real use case. Tell us how you work with music and what you need to understand. We’re inviting a small group to help shape TimbreSift.
Participation is invitation-only. An access request does not guarantee an invitation.