Sonic splitter @AudioshakeAi. Author of The Big Disruption. Fiction in @nytimes @VICE @WIRED. Mostly noise, some signal.

San Francisco
I have no idea how this happened but...so excited and proud of our team!!! Audio nerds unite!
Speechless (insert vocal isolation joke). Thank you @TIME for recognizing AudioShake’s stem separation technology as one of the Best Inventions of 2023. More on why in the year of AI everywhere, our work stood out: time.com/collection/best-inv…
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Fun fact for the audio nerds: live broadcast gives you roughly 10–15 milliseconds to process audio before latency becomes a problem. That’s one reason source separation has historically lived in post-production. Unlike noise reduction, you can actually separate the speech from the background, BUT it’s been too slow. We’ve been working on changing that at @AudioShakeAI. Dialogue RT separates speech from background audio in 11 ms end to end. So out of post and into live. Kinda wild to watch individual sounds disappear in real time.
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3+ years on, this remains my sole superpower
My super power is that I can guess within 10 seconds of entering any venue / bar / restaurant whether they will play The Shins at some point during my stay.
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jessica powell retweeted
Dialogue RT isolates dialogue from a live feed in 11 ms end to end — measured model input to isolated output. That's not noise suppression. It's two stems: dialogue, and everything else. On NVIDIA DGX Spark and Blackwell architecture GPUs. audioshake.ai/post/isolating…
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jessica powell retweeted
Four days at IBC, hundreds of conversations. The question we heard most: “Can it run on the feed, not the file?” Answer: yes. Live, less than 11 milliseconds. Another great year in the books.
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jessica powell retweeted
Here's where to find us at IBC all week! AudioShake — 14.F46 (live Dialogue RT demos) AI-Media – 5.C34 Dell — 14.D43 Ortana — 1.C37h ScorePlay — 1.D47 Telos Alliance — 8.D37 Book time now: tidycal.com/audioshake/ibc26
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Today we're releasing @AudioShakeAI Multi-Speaker 2.0: one recording of several people talking goes in, a clean labeled track per person comes out — including the moments they talk over each other.
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7/ Every job also returns confidence scores, per 20 ms frame and per file: how sure we are the speech went to the right speaker, and how cleanly the overlapping voices came apart. Rank a million hours, skip the unusable parts.
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Coming out of X hibernation to unveil our new brand vision for @AudioShakeAI , courtesy of my 12-yr-old. P.S. We have a bet on whether I can get more likes than his Brawl Star YT videos, so help me out here.
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definitely going to lose this bet, though i guess i deserve it since have been off X for so long 😭
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jessica powell retweeted
"Music is more durable than more strictly cognitive tasks like coding." Why? The core of music lives in live performance, learning an instrument, and the artist-fan connection. The stuff that's hardest to automate is exactly the stuff we value most. Great clip from @trapital 👇
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When the recording is the only copy that exists, you can't let a tool guess. Suppression smears the words. Generative enhancement invents them. So @AudioShakeAI built Speech Recovery to do neither — it isolates the speech that's actually there, and adds nothing.
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There is so much media & archives blocked from streaming & social platforms bc of copyright compliance. Distribution demand has multiplied, and the compliance workflows underneath it have not. Today we're launching the system that closes that gap. tinyurl.com/2nyduuy9
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1. Music detection. 2. Music removal (yes---removing music, including with song lyrics--from a mixed media file) 3. Music rights identification 4. Cue sheet creation A great, practical example of source separation applied to a real-world problem!
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