Microphone Noise Suppression DSP: Spectral Subtraction vs Deep Learning Filters: Competitive Benchmark Analysis and Matériel Interactions

[Mason Cox]

I’ve been running benchmarks on the two main approaches to mic noise suppression and want to know what you’re all seeing in real competitive setups. For spectral subtraction—like the older NVIDIA Voice Activity or basic RNNoise—how much voice fidelity are you actually losing on high-frequency consonants when pushing the attenuation knob? I’m getting sibilance smearing on my “s” sounds around the -18dB threshold, which kills callout clarity in ranked.

On the deep-learning side (RTX Voice, Broadcast, Krisp), I’m really after the latency delta question.

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[Blake Mitchell]

Short answer: it depends entirely on what kind of noise you’re fighting and what your rig can afford in CPU budget. Spectral subtraction is still the go-to for steady-state noise like fan whine or AC rumble—it’s deterministic, near-zero latency, and basically free on the CPU. But it falls apart hard against impulsive noise; mechanical keyboard clacks and mouse buttons turn into that characteristic “musical noise” artifact mess, where you hear these ghostly tonal blips instead of clean transients. That’s exactly where deep learning wins—RNNoise, Krisp, Broadcast-style models actually recognize the noise as noise and nuke the keypresses while keeping your voice intact. The tradeoff is CPU cost and latency. RNNoise is shockingly light (single-threaded, ~10ms), but heavier models can eat CPU cycles you’d

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[Alex Harris]

Real talk from the bench: the biggest misconception I see is treating this as purely an audio-quality debate when, for competitive play, it’s really a latency and CPU overhead fight. Spectral subtraction (the classic approach behind things like Krisp’s older models and most built-in Discord/SteelSeries DSP) is cheap — you’re estimating a noise profile during voice pauses and subtracting a magnitude spectrum, so CPU footprint stays minimal and latency is basically nil. That matters when you’re already running a game at 240Hz and can’t afford a neural net stealing cycles from your render thread. The tradeoff is musical noise — those artificial-sounding random tonal artifacts when the subtraction overshoots — which gets brutal on sustained fan noise

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[Tyler Rogers]

Great topic. In my testing across a few competitive titles, the practical difference between the two comes down to latency budget vs. artifact tolerance. Spectral subtraction (like the older Krisp/SpeechRecognition baselines or what’s baked into some cheap USB caps) is cheap on the CPU and runs at near-zero latency, which matters when you’re trying to hold sub-20ms voice chain delay in a ranked squad. But the trade-off is musical noise—the classic over-subtraction artifact where residual noise gets chopped into those faint whistling tones during quiet pauses. During intense firefights with fan noise and keyboard clatter, spectral subtraction tends to chew up vocal clarity and make teammates sound robotic. Deep learning filters—RNNoise, NVIDIA Broadcast, Krisp—handle that non-stationary noise far better because they’re actually modeling speech vs. noise separation rather than just subtracting a noise floor. The catch is overhead: NVDA Broadcast can eat 8-15% of a core on the GPU and adds 15-30ms

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