The real world is noisy — fans, traffic, keyboard clicks, hiss. A voice AI has to pull your voice out of all that mess before it can understand you (and before you sound clear on a call). Let's clean up a noisy signal.
🟩 is the clean voice (faint reference). Add noise and it gets buried (🟥). Turn up the denoiser to clean it (🟦) — but careful: too much and you smother the voice itself and it goes muffled! Find the sweet spot.
background noise level
denoiser strength (too much = muffled voice!)
The smoothing/gate you used is the classic idea. It works, but it can leave a weird "underwater" sound and muffle the voice. Modern apps use AI:
✅ Fast, tiny, runs anywhere. ⚠️ Leaves "musical noise" artifacts and can muffle the voice.
Estimates the noise in the quiet bits and subtracts it from the spectrogram. ✅ Better than a plain gate. ⚠️ Still leaves artifacts.
A net trained on tons of noisy+clean audio removes keyboard clicks, traffic, even babble while keeping the voice natural. ✅ Sounds amazing (this is what Zoom/Discord use). ⚠️ Needs a model.
You did noise suppression — pulling a clean voice out of a noisy signal, and learning the trade-off: too little leaves hiss, too much muffles the voice.