Noise pollution is part of modern life, and technology has produced two very different answers to it. Active Noise Cancellation (ANC) cleans the sound reaching your ears from the environment around you, like the headphones people wear on airplanes. AI-based noise cancellation algorithms filter noise out of a microphone stream (what you send) and a headphone stream (what you hear) for real-time communication.
The two are easy to confuse because they share a name, but they solve different problems. ANC is perceived directly in your ear. AI-based microphone noise cancellation removes the noise that passes through a microphone, so your voice comes through cleanly on calls and recordings. This guide breaks down how each one works, where they differ, and when you’d reach for one over the other. For the consumer-level basics, see our guide on how noise cancelling works.
The core difference
ANC relies on physical mechanisms to block or reduce external noise. You’ll find it in noise-cancelling headphones, earplugs, and even the acoustic design of buildings. It uses microphones to capture external sound and generates a sound wave 180 degrees out of phase with the incoming noise, canceling it out through destructive interference.
AI-based noise cancellation is software. It uses machine learning to analyze and process sound in real time and remove noise from the audio stream. Because it’s software, it runs across a range of digital devices and applications, from laptops and smartphones to wearables and smart speakers, and it can be updated and improved over time.
That leads to three practical differences:
- Hardware vs software. ANC needs specific hardware (mics, a processing chip, speakers). AI-based algorithms run on devices you already own.
- Fixed vs adaptable. ANC is tuned for particular environments and noise types. AI models learn from data, so they adapt to different accents, noises, and conditions.
- Latency. Analog ANC reacts almost instantly. AI-based algorithms run in real time but add a small processing buffer, measured in milliseconds.
Here’s the full comparison at a glance:
| Dimension |
Active Noise Cancellation (ANC) |
AI-based Noise Cancellation |
| What it is |
Hardware, analog acoustics |
Software, machine-learning algorithm |
| How it works |
Emits an inverted “anti-noise” wave (destructive interference) |
Neural network separates the voice from the noise and removes the rest |
| Hardware needed |
Microphones, DSP chip, and speakers built into the device |
Runs on devices you already have |
| Best at |
Steady, low-frequency noise (engines, HVAC hum, traffic) |
Voices, chatter, and sudden or changing noise |
| Weakest at |
Sudden and high-frequency sounds |
Heavy compute; can distort the voice if tuned too aggressively |
| What it cleans |
The sound reaching your own ears |
Both your outbound mic and the inbound audio from others |
| Adaptability |
Tuned per device; adaptive models adjust the level |
Learns from data and improves with updates |
| Latency |
Near-instant (analog) |
Real time, with a small algorithmic buffer |
| Typically used in |
Headphones, earbuds, cars, appliances |
Calls, meetings, VoIP, contact centers, voice AI |
Human and noise touchpoints
To tell these tools apart, it helps to separate the two ways noise reaches a listener. There’s noise from the real world that reaches your ears directly from the environment, and noise from virtual environments that arrives through a device, like a phone or headset on a conference call. ANC addresses the first. AI-based algorithms address the second.
Noise from the real world
There are two kinds of headphone noise cancellation, used alone or together.
Passive noise cancellation (noise isolation) is the simplest and most common. It blocks external sound with insulating materials between the ear and the outside world, like the seal of an earcup or in-ear tip. It’s cheap and works well against higher-frequency sound.
Active noise cancellation adds electronics. It cancels incoming sound with an inverted wave, and it works best when both signals are nearly identical except for the inversion, which drives them toward silence. ANC quality varies a lot between products, depending on the reference microphones and the post-processing.
ANC systems use two microphone placements, often combined:
- Feedforward microphones sit on the outside of the earcup and capture sound before it reaches the ear canal, so the processor can build the anti-noise wave early.
- Feedback microphones sit inside, near the speaker, and listen to what actually reaches the ear, so the processor can correct anything that leaked through the seal.
- Hybrid ANC uses both an outer and an inner mic together, and it’s the most common design in premium headphones.
Placement changes what each design is good at. This table sums up the main attenuation types, including passive isolation and adjustable ANC:
| Attenuation type |
Mic location |
Strengths |
Weaknesses |
| Isolation (passive) |
N/A |
No extra power needed; avoids sensory issues |
Low-frequency attenuation is very poor |
| Feedback ANC |
Inside the ear cup |
Better at canceling wind noise |
Poorer high-end attenuation |
| Feedforward ANC |
Outside the ear cup |
Better at canceling mid frequencies |
Poorer incidental noise attenuation |
| Hybrid ANC |
Outside and inside the ear cup |
Best of both types, with the drawbacks of neither |
Usually more expensive |
| Adjustable ANC |
Adjustable |
Lets you turn the ANC effect down |
Requires manual interaction |
High-end ANC headphones may use up to eight microphones to separate wanted sound from unwanted sound, which produces more accurate cancellation. The same principle now shows up beyond headphones: cars and home appliances capture noise with microphones or sensors, then a digital signal processor (DSP) generates opposing sound waves to cancel it.
The catch with ANC is its fixed design. It excels at steady, low-frequency noise like an airplane engine or traffic rumble, but it’s much weaker against sudden or high-frequency sounds like human voices or a barking dog.
Noise from virtual environments
Calls and virtual meetings carry their own noise: microphone hiss, background chatter, environmental sound, and artifacts introduced by the network itself. Audio in these environments is dynamic. It passes through codecs and filters, involves multiple speakers on different microphones, and changes quickly. Classical ANC isn’t built for that.
This is where AI-based noise cancellation, like Krisp, comes in. Instead of generating an anti-noise wave, it uses trained neural networks to tell a human voice apart from everything else and remove the rest in real time. A few things worth knowing about how these algorithms work:
- They learn from data. The model trains on large amounts of clean speech mixed with many kinds of noise, so it generalizes to noises and accents it was never explicitly programmed for. That’s why it handles the unpredictable sounds ANC struggles with.
- They often combine DSP and deep learning. Classical methods like spectral subtraction estimate and subtract a noise profile; modern systems layer a neural network on top. RNNoise is a well-known hybrid that pairs signal processing with a small recurrent network for speed.
- They clean both directions. Krisp removes noise from the outbound stream (your microphone) and the inbound stream (what you hear from other participants), and it also handles echo.
- They run on-device. Processing happens locally, so audio isn’t sent to a server just to be cleaned.
The result is noise cancellation built for communication rather than personal listening, which is a different job than the one ANC headphones were designed for.
So which one do you need?
They’re complementary, not competing:
- ANC is for quieting the world for the person wearing the headphones: planes, trains, open offices.
- AI-based noise cancellation is for sounding clear to the people on your call, since ANC does nothing for what your microphone transmits.
In a noisy home office, the strongest setup uses both: ANC headphones to help you focus, and AI-based noise cancellation so the other side hears only your voice. As models keep improving, the software side of this pairing is advancing the fastest.
Krisp licenses its noise cancellation, echo cancellation, and voice-AI models as SDKs you can embed directly into applications and devices. Learn more about Krisp’s SDKs to start an evaluation.
For a related deep dive, see our explainer on acoustic echo cancellation.
FAQs
What's the difference between ANC and AI-based noise cancellation?
ANC is hardware that cancels ambient sound acoustically before it reaches your ear, using an inverted anti-noise wave. AI-based noise cancellation is software that removes noise from an audio stream so the other side of a call hears only your voice. ANC quiets the world for you; AI cleans what your microphone sends.
Can you use ANC and AI-based noise cancellation together?
Yes, and it’s the best setup for calls in a noisy space. ANC headphones keep you focused, while AI noise cancellation keeps your outgoing audio clean for everyone else on the call.
Which is better for video calls?
AI-based noise cancellation. ANC does nothing about the noise your microphone picks up and transmits, so on a call it’s the software layer that keeps your voice clear.
Does AI noise cancellation need special hardware?
No. It runs in software on the devices you already use, sitting between your microphone and your meeting app, so you don’t need ANC headphones to sound clear.
