I've been using Krisp for several months now, primarily during remote meetings where I need to mute persistent background noise (keyboard, fan, etc.). Its performance on those types of noise sources is well-documented. However, I'm now analyzing a more nuanced scenario: intentional background audio, specifically music.
My core question stems from a common hybrid work situation: if I'm working from a café or have low-volume music playing in my home office, how does Krisp's AI filter classify and handle that signal? Does it treat it as "noise" to be removed alongside keyboard clatter, or does it recognize it as a coherent audio stream to be preserved?
From my initial, non-systematic tests, the behavior isn't always consistent. I'm interested in the community's experiences and any technical insights into the model's logic.
**Key variables I'm considering:**
* **Volume & Source:** Does the outcome change if the music is from a nearby physical speaker versus played through my own headphones (using audio loopback)?
* **Music Genre:** Is a consistent, classical piano piece treated differently than percussive, vocal-heavy pop music?
* **Krisp Mode:** Are there differences between the "Voice" and "Noise" cancellation modes in this context?
My hypothesis, based on Krisp's training to isolate the human voice, is that most non-voice audio is likely suppressed. This could be problematic for scenarios like sharing a musical snippet during a call or participating in a virtual event where ambient music is part of the atmosphere. I'm looking for a detailed breakdown of what parameters influence this filtering decision.
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Great question. I've tested this scenario a fair bit while working from home with a smart speaker playing music at low volume.
In my experience, Krisp does tend to filter out background music, treating it more like noise than a voice. The consistency seems tied to volume. At very low, ambient levels, it's often removed completely. If I turn it up just to the point where it's clearly audible in the room, sometimes the music's higher-frequency elements (like a hi-hat or vocals) will start to bleed through sporadically, which is actually more distracting than full removal.
Your point about audio source is key. I found music from a physical speaker gets filtered more aggressively than audio routed through a loopback device, where Krisp seems to struggle more to separate the streams. I haven't noticed a big difference based on genre, but the "spikiness" of the audio might be a factor.
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That's a really helpful observation about the audio source. I hadn't even considered the difference between a physical speaker and loopback audio, but it makes sense the AI would be tuned for environmental noise first. Your point about higher frequencies bleeding through at a certain volume is exactly what I'm trying to avoid, it sounds so messy.
I wonder if the inconsistency we're both noticing means the filter is constantly making a judgment call between "background sound" and "interference." Maybe music with a steady beat or repetitive melody gets flagged as noise more easily than something more erratic? I'm just guessing here, but it feels like it's on a spectrum, not a simple on/off switch for music.
Have you found any settings tweak that helps, or is it just a matter of keeping the volume impossibly low?
Interesting guess about the filter making a judgment call. I'm wondering if it's less about the type of music and more about how the AI model is fundamentally trained. If its primary job is to isolate a single human voice, anything that isn't a voice pattern might just get flagged for suppression, regardless of whether it's pleasant music or an AC unit.
Your question about settings is spot on. I've looked, but Krisp's controls seem pretty binary - on or off, maybe with a toggle for "aggressive" noise cancellation. I haven't found a "preserve ambient audio" slider, which is maybe what we'd need for this specific case.
It makes me wonder how other tools, like NVIDIA Broadcast or some dedicated DAW plugins, approach this. Do they have more granularity, or is preserving background music just a fundamentally harder problem than killing random noise?
Great, you're testing the exact scenario I ran into during my CRM migration project! My team works in a shared space, and someone always has lo-fi beats playing.
From my tests, the *source* is your biggest variable. Music from a physical speaker in the room gets classified as noise and removed almost every time, especially at cafe volume. But if you're using a virtual audio cable/loopback to inject music directly into your mic input, Krisp gets confused. It'll try, but you'll get that sporadic bleed-through user750 mentioned, which is awful for meetings.
On your music genre question, I haven't seen consistent differences. The model seems trained on spectral patterns, not genres. A steady piano melody and a keyboard's constant clacking might share a similar "non-voice, continuous" pattern to the AI.
Have you tried comparing it side-by-side with something like Discord's Krisp implementation? I found it behaves slightly differently than the standalone app.