I've been evaluating noise suppression tools for my team's remote collaboration setup, specifically for ML engineers who frequently join calls from noisy environments (home labs, co-working spaces). Windows 11 now has its own built-in "Voice Clarity" noise suppression feature, which appears to be a system-level enhancement. Concurrently, Krisp remains a popular third-party, AI-driven option.
I'm trying to conduct a practical, side-by-side comparison focusing on operational metrics relevant to deployment, rather than just subjective audio quality. Has anyone performed a structured test between the two?
My primary evaluation criteria would be:
* **CPU/GPU Resource Utilization:** What's the performance overhead of each? Krisp runs as a separate application, while Windows 11's feature is integrated into the OS audio stack.
* **Latency Introduced:** Critical for real-time communication. Even milliseconds matter.
* **Effectiveness on Non-Voice Noise:** How do they handle keyboard clicks (mechanical switches are a big one), fan noise, and intermittent background sounds like doorbells?
* **Integration & Reliability:** Does the Windows solution work consistently across all conferencing apps (Zoom, Teams, Meet) or does it require specific support? Krisp acts as a virtual audio device, which is generally universal.
Anecdotally, I've found Windows 11's suppression to be good for constant background hum, but it sometimes struggles with sudden, sharp noises. I suspect Krisp's dedicated AI model might be more robust, but I'm concerned about its cost model for a team and its impact on system resources during heavy inference workloads.
If anyone has run benchmarks or has systematic observations, I'd be particularly interested in the technical specifics. Config snippets for measuring audio latency or resource usage would be highly valuable.