AI Noise Cancellation for Calls and Video Meetings

How AI Noise Cancellation Works

Traditional noise cancellation (like in ANC headphones) works by generating inverse sound waves that physically cancel out ambient noise in your ears. AI noise cancellation for calls works completely differently. It processes the audio signal digitally, using a neural network to separate human speech from everything else in the audio stream.

The neural network is trained on millions of samples of speech mixed with various noise types: traffic, crowds, music, typing, pets, appliances, wind, and more. It learns what speech sounds like (fundamental frequency patterns, formant structures, temporal patterns unique to human vocal production) versus what noise sounds like (continuous hums, irregular bursts, broadband sounds without speech structure). During a call, the model runs in real time on every audio frame, keeping the speech components and discarding the noise components.

Modern AI noise cancellation runs locally on your device rather than in the cloud. This is essential for latency: cloud processing would add 100-300 milliseconds of delay, which is noticeable and disruptive in conversation. Local processing adds less than 10 milliseconds of latency, which is imperceptible. Krisp pioneered this approach, building lightweight neural networks that run on standard laptop hardware without requiring a dedicated GPU.

What AI Noise Cancellation Can Remove

Modern AI noise cancellation handles a remarkably wide range of sounds. The effectiveness varies by noise type:

Very Effective (95%+ removal)

• Continuous background noise: Fan hum, air conditioning, refrigerator buzz, office HVAC. These steady-state sounds are the easiest for the model to separate from speech because their acoustic profile is consistent and distinct from human voice patterns.
• Keyboard typing: The sharp, repetitive click pattern of keyboard input is one of the most common and most distracting meeting noises. AI handles it extremely well, removing individual keystrokes without affecting the speaker's voice.
• Traffic and street noise: Cars, trucks, horns, and general urban ambient noise are well-represented in training data and consistently filtered.

Effective (80-95% removal)

• Barking dogs and pet sounds: Single barks and whines are filtered well. Sustained, loud barking close to the microphone can sometimes bleed through partially.
• Construction and home renovation: Drilling, hammering, and power tools are filtered reasonably well but may briefly affect voice quality during very loud bursts.
• Music and television: Background music and TV audio are typically removed, though very loud music with vocals can occasionally confuse the model because it detects vocal patterns.
• Baby crying and children: Generally filtered well, though a loud cry very close to the microphone can challenge the model.

Challenging (variable results)

• Other people talking nearby: This is the hardest case because the model is trained to preserve speech. If someone is talking in the background, the noise cancellation must distinguish between the speaker you want to hear and the speaker you do not. Results depend on volume difference and spatial separation. A conversation in the next room is usually handled. A person talking at the same volume right next to you may partially bleed through.
• Echo and reverb: Room echo from hard surfaces is partially addressed by some tools but is better handled by echo cancellation algorithms separate from noise cancellation.

Impact on Meeting Transcription Accuracy

The connection between noise cancellation and transcription quality is direct and measurable. Automatic speech recognition models achieve their best accuracy on clean audio. Background noise introduces errors because the ASR model sometimes misinterprets noise artifacts as speech or fails to hear spoken words through the noise.

In testing, AI noise cancellation has been shown to improve transcription accuracy by 5 to 15 percentage points in moderately noisy environments. In a quiet office, the difference is minimal because the audio is already clean. In a coffee shop, home with children, or open-plan office, the difference is substantial. A transcription that would be 80% accurate with background noise might reach 92% accurate with AI noise cancellation applied before the audio reaches the ASR engine.

This improvement cascades into every feature built on the transcript. Meeting summaries become more accurate because the underlying transcript has fewer errors. Action items are less likely to be missed because the speech that describes them was captured cleanly. Speaker diarization improves because the voiceprint analysis has less noise contaminating the vocal characteristics. Investing in noise cancellation is one of the highest-leverage things you can do to improve the overall quality of your AI meeting tools.

Software vs Hardware Noise Cancellation

There is often confusion between hardware noise cancellation (ANC headphones) and software noise cancellation (AI processing). They serve different purposes and complement each other rather than competing:

Hardware ANC (headphones)

Active noise cancelling headphones cancel noise in your ears so you can hear the meeting better. They do not affect what your microphone picks up. If you are in a noisy environment wearing ANC headphones, you hear the meeting clearly, but the other participants still hear all the background noise from your microphone. ANC headphones solve the listening problem, not the sending problem.

Software AI Noise Cancellation

AI noise cancellation processes your microphone audio before it is sent to other participants. It solves the sending problem: other people hear clean audio from you regardless of your environment. It does not help you hear better. Using both together gives you the full benefit: you hear clearly (hardware ANC) and others hear you clearly (software AI).

Microphone Quality Still Matters

AI noise cancellation works better with better microphones. A dedicated USB microphone or headset microphone positioned close to your mouth gives the AI a higher signal-to-noise ratio to work with. The speech is louder relative to the background noise, which makes the separation task easier. Laptop built-in microphones pick up more room noise and less direct speech, giving the AI a harder job and producing slightly lower quality results.

Privacy Considerations

AI noise cancellation that runs locally on your device (like Krisp) processes audio without sending it to external servers. This is an important privacy feature for several reasons. First, background sounds in your environment can inadvertently reveal personal information: conversations with family members, medical devices, location-specific sounds, or discussions happening in the room. Local processing means this audio never leaves your computer.

Second, some organizations and industries have policies against transmitting audio to third-party cloud services, particularly in healthcare (HIPAA), finance, and government contexts. Local-processing noise cancellation meets these requirements because the audio stays on the device. The neural network model runs locally, processes the audio in memory, and outputs the cleaned audio directly to the call application.

Cloud-based noise cancellation alternatives do exist and can sometimes offer higher quality because they can use larger models that would be too resource-intensive for local processing. The trade-off between quality and privacy depends on your requirements. For most business use cases, local processing quality is excellent and the privacy advantage is worth it.

Setting Up Noise Cancellation for Your Team

Deploying AI noise cancellation across a team is straightforward because most tools work as a system-level audio processor that sits between your microphone and your meeting application:

For IT administrators deploying across an organization, most tools offer centralized license management and silent installation options. Some integrate with MDM solutions for managed device deployment.