App: Hush | AI for Spoken Audio
Developer: Ian Sampson
Genres: Utilities, Music

Powered by machine learning, Hush removes background noise and room reflections from recorded speech — without audible artifacts or loss of clarity. The result preserves all the nuance of the original signal, as if it were recorded in a well-treated studio.

HEAR IT FOR YOURSELF.
Check out the audio demo at hushaudioapp.com.

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MUTE ALL THE NOISE.
Hush filters out broadband sounds like ventilation, appliances, traffic, and wind. Restore audio from imperfect environments and produce studio-quality recordings wherever you are.

MAKE EVERY ROOM ANECHOIC.
Reduce room reflections and comb filtering — from untreated condos to massive auditoriums. Crank up the dial to absorb every trace of reverb, or mix in some dry signal for a more natural sound.

DECHIRP. DEBARK. DEHONK.
Most noise reduction algorithms struggle to remove transient sounds like chirping birds, barking dogs, or honking horns. Hush eliminates them with surgical precision, saving you hours of manual editing.

AUTOMATE YOUR WORKFLOW.
Hush accepts individual audio files or whole batches, making short work of dialogue stems, voiceover auditions, or audiobook chapters. Choose an output folder and let Hush clean up all your audio in one go.

STAY COOL WITH APPLE SILICON.
On M1 and M2 Macs, Hush uses the energy-efficient Neural Engine, leaving your chassis cool, your fans off, and your CPU free for other tasks. It’s also blazing fast, processing an hour-long podcast in a matter of minutes.

OWN IT FOR LIFE.
Neural networks with this much power usually require a server — along with hefty usage or subscription fees. Instead, Hush runs entirely on your Mac. Your data stays private, and the app is yours forever.

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REQUIREMENTS
Apple Silicon (M1 or M2) strongly recommended, eGPUs and Intel CPUs also supported.

CONTACT
Any questions or feedback? Drop me an email at ian@hushaudioapp.com.

Release notes:
This version resolves a performance issue on macOS Sonoma, speeding up rendering and eliminating some common errors.