Microphone Noise Suppression DSP: Spectral Subtraction vs Deep Learning Filters: Advanced Implementation and Real-World Architecture

[Luke Parker]

I’ve been benchmarking spectral subtraction (Wiener-filtered variants) against RNNoise-style models for a real-time suppression pipeline, and I’m trying to figure out where the practical cutoff is. For eliminating mechanical keyboard transients specifically—those sharp click bursts in the 2-5 kHz range—spectral subtraction keeps introducing musical noise artifacts that wreck voice clarity during callouts, but I’m not sure if that’s a smoothing parameter issue or a fundamental limitation. For anyone who’s actually deployed these in a low-latency competitive setup: what’s the largest latency budget you can tolerate before you notice it during voice comms, and how did you measure that delta end-to-end?

When you moved to a deep learning approach, what inference framework did you run at runtime—ONNX Runtime, TensorRT, or a custom CUDA kernel—and did you see frame-based models like RNNoise (20ms windows) hold up under bursty mechanical input, or did you need context-aware architectures to catch rapid key presses? Also, how did you handle the CPU/GPU tradeoff on mid-range hardware without blowing past your frame time budget?

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[Blake Morgan]

Great topic. On the spectral subtraction side, most of the practical gains come from how you handle the over-subtraction factor and spectral floor rather than the algorithm itself. The classic approach (Boersma / spectral subtraction with Wiener post-filtering) is still extremely relevant for competitive play specifically because it’s near-zero-latency and runs comfortably on CPU. The key pain point is musical noise—the random tonal artifacts that show up when your noise estimate drifts during quiet passages. You suppress that by using a smoothed noise profile with adaptive tracking (catch the minimum statistics during silence windows) and applying a spectral floor around -20 to -25 dB so you’re not amplifying the residual noise floor. If you’re building a real-time pipeline, stick with a 20-32ms STFT window; going shorter kills low-end fidelity on voice, longer adds latency you don’t want in ranked comms.

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[Isaac Smith]

Honestly, for competitive gaming the latency argument heavily favors well-tuned spectral subtraction, and I’d push back anyone who assumes deep learning is automatically “better.” A properly implemented Wiener filter with over-subtraction alpha around 2 and noise floor estimation updated during game lull periods gives you single-digit millisecond latency at near-zero CPU cost—stuff you can’t always say for DL models. The real enemy of spectral subtraction is musical noise, and the fix is almost always temporal smoothing on the gain function plus a spectral floor clamp rather than cranking the over-subtraction factor, which just mangles your high-end. If you

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[Luke Parker]

Followed the advice, and the noise suppression is finally working properly — I ended up going with a hybrid approach like u/AudioPhileDev suggested, running a light spectral subtraction front-end to knock out the steady-state hiss before feeding the signal into the deep learning model, and the artifacts are basically gone now. I was getting those annoying musical-noise artifacts with pure spectral subtraction, but once I tuned down the over-subtraction factor and let the neural filter handle the transient pops and keyclacks, the output sounds clean without that robotic underwater quality. Big thanks to everyone who pointed me toward the Wiener-filter post-processing step too, that was the missing piece for keeping the sibilants crisp. Appreciate the help, guys!

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