AudioShake

Multi-Speaker 2.0 is live–the world’s first low and high-resolution multi-speaker separator. One recording of several people in. A clean labeled track per person out, including the moments when two people talk at once. 8 kHz to 48 kHz, one model. Confidence scores every 20ms.

A finished mix doesn’t have to be final. Sun 11/1, 1pm at #AESShow Nashville: Suzanne Kirkland on the Focusrite stage on how Judy Garland’s vocal was separated from the original mono of The Wizard of Oz for Sphere. Plus John Merchant on the Bee Gees 🎶

A finished mix doesn’t have to be final. Sun 11/1, 1pm at #AESShow Nashville: Suzanne Kirkland on the Focusrite stage on how Judy Garland’s vocal was separated from the original mono of The Wizard of Oz for Sphere.

Headed to #AESShow Nashville, Oct 30 – Nov 1. Bring a mix you’re stuck on to booth #521, and watch us separate it for you.

Expired music license? Detect it, identify it, remove it, replace it. Jessica Powell with @AWS at #IBC2026

Heading to Sacramento Tech Week? Swing by RELAY 001 tomorrow. Attendees get AudioShake credits to separate stems and work with source audio in new, creative ways. Fri 10/23 · 5–8 PM PT RSVP: luma.com/erxl3n30

What does a Lite-Brite have to do with AI? A finished mix is one picture made of many pegs. Source separation pulls every peg of one color (vocals, dialogue, effects) onto its own board. @themoko explains 👇 audioshake.ai/post/what-is-a…

Accessibility isn’t a someday project anymore, and that includes the archive. Captions, audio description, and dubbing all need dialogue and M&E on separate tracks. Most catalog titles only have the finished mix. We separate them, so the work can start. 👇

Live dialogue isolation has a 9 to 15 ms window before lip-sync slips. Dialogue RT runs at 11 ms on a single Dell Pro Max with GB10. Dell wrote up how it got from an NAB demo to production: dell.com/en-us/blog/more-tha…

Editing a film or ad and need "a song about starting over"? You can now search @audionetwork's catalog by lyrics, not just genre and mood 🎶 Built with @AudioShakeAI's lyric transcription + alignment API. Case study: audioshake.ai/case-studies/u…

A finished mix used to mean dialogue, music and effects were fused for good. At Mix Sound for Film, Suzanne Kirkland and Stephen Oliver shared what changes when they're not: archives that can be reused, sports libraries that work across streaming, and more localized content.

Most voice AI demos run on clean audio. Real callers interrupt, talk over each other, + put you on speaker in the car. Thanks @RimeLabs, @LilyJClifford, @BrookeHopkins (@CovalDev), Josh Burnell (@Asurion) and @ShivIndap (@Upstart) for digging into that gap with us at #SFTechWeek

"Enormous amounts of that conversation already exist. The challenge is in turning it into usable training data." @themoko on @AudioShakeAI’s Refinery. audioshake.ai/products/refin…

Real conversation is messy and overlapping. Most training data isn't. The Refinery splits real-world recordings into one track per speaker and scores each for quality. Hundreds of millions of minutes processed with frontier labs. audioshake.ai/products/refin…

Most audio in the world was never recorded on separate tracks. @themoko breaks down source separation on @Dell's #ReshapingWorkflows with @LoganatDell, starting with how we pull vocals, instruments, or a single speaker out of a finished mix.

Most archive audio is stuck in a finished mix, which makes dubbing, clearing music and re-licensing hard. Suzanne Kirkland talked through how @AudioShakeAI's separation, @ortanamg, and @AWS Marketplace fit together to fix that, with Ortana and AWS at IBC.

Voice agents fail in predictable ways: overlapping speakers, background noise, crosstalk the model was never trained to hear. On Wednesday, @themoko and @lilyjclifford (@RimeLabs) get into what teams learn when they build that stack themselves. RSVP: partiful.com/e/qRX76YKI93J2I…

NVIDIA's Nemotron 3 diarizer hit #1 on Voice Arena. We ran it against ours on LibriCSS (8-speaker recordings). Diarizer vs. diarizer, no overlap: 35% fewer word errors. With Multi-Speaker isolating each voice, all recordings: 52% fewer word errors than Nemotron 3.

Upload a file → get a cue sheet back. @AudioShakeAI's music ID is now in @MusicReportsInc' Cuetrak®. We separate the music out of the mix first, then identify each cue — so buried and unlabeled tracks still get caught. audioshake.ai/press-releases…

A therapy podcast with 70+ episodes was about to lose its whole archive in a station move — licensed bumper music, baked into the mix, no session files. AudioShake's Music Removal stripped the cues out of every episode, leaving the speech untouched. audioshake.ai/case-studies/f…