I was reviewing my OBS setup for a new webinar series and ran a cost/performance analysis on my audio pipeline. I made a significant discovery: Krisp's processed output can be used as an input source for *other* audio filters within OBS Studio. This fundamentally changes the efficiency model.
Previously, I viewed Krisp as a terminal node in the audio chain—a final, all-in-one noise suppression layer. The workflow was linear: Mic Input -> Krisp -> Output. However, by adding Krisp via the Application Audio Capture source (or the Virtual Mic), its output becomes a pristine, noise-free signal that you can then feed into additional effects.
**A practical example of my new chain:**
* **Source:** USB Microphone (Hardware Input)
* **Layer 1:** Krisp (via Krisp Virtual Mic, set as Input Device in OBS)
* **Layer 2:** OBS Noise Suppression (light, for residual hiss)
* **Layer 3:** Compressor/Gain (to normalize levels post-processing)
* **Layer 4:** Limiter (final safety)
This layered approach allows for a more granular total cost of ownership. You can use Krisp for its superior AI-driven noise cancellation (its core competency), then apply lighter, CPU-cheap native filters for fine-tuning. It's akin to using a reserved instance for baseline coverage and spot instances for variable, fine-grained workloads.
The performance uplift is notable. By letting Krisp handle the heavy lifting of background noise, the subsequent OBS filters operate on a cleaner signal, reducing their processing load and potential artifacts. For anyone doing professional streaming or recording, this composability is a major architectural benefit.
—Jake
Show me the bill.
That's a really interesting technical approach. It makes sense to treat Krisp as a high-quality preprocessing layer rather than a final output. Your point about the CPU cost distribution is something I hadn't considered.
I do have a practical question, though. In your layered chain, how do you handle monitoring? When you're listening to your own voice through the OBS monitoring output with that many filters stacked, is there a noticeable latency that throws you off? I've always been cautious about adding too many real-time effects for that reason.
That's a smart way to frame it. I've been using a similar layered approach for my live product demos, but I treat Krisp's virtual mic as the final step before OBS, not the first. I route my raw mic through a VST plugin chain in a DAW for EQ and compression, then send that to Krisp's virtual input, and finally capture that output in OBS. It lets me use my preferred, more surgical processing tools before the noise gate.
Your method puts the heavy AI lifting first, which probably saves a bit of CPU since Krisp is working on a less processed signal. Have you run any tests on whether the order impacts the quality of Krisp's suppression? I'm curious if feeding it a compressed signal changes its behavior.
Sample size matters.
Oh, the classic "layered approach" and "granular total cost of ownership" for an audio chain. I have to ask, is this a production studio or a one-person webinar? You're describing a multi-layered processing stack that introduces multiple points of failure and complexity for a marginal, likely inaudible, improvement.
My fundamental skepticism is about the premise. You're using Krisp, a proprietary AI model that's essentially a black box, to produce a "pristine" signal. Then you're immediately distrusting that output enough to layer OBS's own noise suppression on top of it. If Krisp needs a follow-up noise filter to catch "residual hiss," then its core competency isn't as superior as the marketing suggests, is it? You're just stacking one processing artifact on top of another.
And the compressor and limiter after all that? You're now dynamically manipulating a signal that has already been non-deterministically mangled by an AI. Have you actually measured the compounded latency or the potential for introducing weird phasing? This isn't efficiency, it's just building a more elaborate Rube Goldberg machine where you can't predict how one layer interacts with another.
Trust but verify.