Is AI noise cancellation really better than traditional ENC? We tested it out.
If you've recently bought headsets, especially UC (Unified Communications) headsets that emphasize "high-definition calls" or "conference use," you've definitely seen these two terms: ENC (Environmental Noise Cancellation) and AI Noise Cancellation. Manufacturers all claim their technology "filters background noise," but which is truly reliable? Is it just marketing hype, or is there a real difference?
As a manufacturer specializing in UC audio equipment, we don't want to rely solely on specifications. So, we moved our lab to the real world-coffee shops, subway stations, and home offices-to conduct blind tests and objective analyses of two of our own headsets equipped with traditional ENC and the latest AI noise cancellation. Here are our findings.
First, let's clarify: what's the fundamental difference between ENC and AI noise cancellation?
Traditional ENC (Environmental Noise Cancellation) has been used for over a decade. It typically relies on two microphones: one facing the mouth to pick up the human voice, and the other facing away from the mouth to pick up ambient noise. The system cancels out background noise through "subtraction." It sounds clever, but it has a fatal flaw: it assumes noise is "stable and predictable"-like the hum of an air conditioner or the sound of a fan. When sudden or complex sounds occur (baby crying, keyboard clattering, sirens), ENC often falls apart, either failing to suppress the noise or even masking your voice, making it sound like you're speaking underwater.
AI noise cancellation is completely different. It doesn't rely on physical cancellation, but on "understanding." The built-in neural network model in the headsets is trained on massive amounts of speech and noise data, enabling it to determine in real time: "This is a human voice, this is a dog barking, this is the rumble of a subway car." Then, it retains only the human voice frequency range, accurately eliminating the others. Crucially, it can recognize "non-steady-state" noise, and it gets smarter with use .
But does this mean AI is always better? Not necessarily. It requires a more powerful chip, more power, and may introduce slight latency. So we decided to conduct real-world testing.
Our testing methodology: No staged lab tests, only real-world scenarios.
We selected two internally developed UC earphones:
Model ENC: Equipped with a mature dual-microphone ENC solution, low cost and low power consumption, targeted at enterprise bulk purchases.
Model AI: Equipped with a self-developed lightweight AI voice separation engine, based on the Qualcomm QCC5181 platform, supporting dynamic noise modeling.
Tests were conducted in four typical scenarios, with 30 seconds of audio recorded for each scenario, completed by three testers with different voice characteristics (male/female/mid-low voice). Evaluation criteria included:
Speech clarity heard by the other party (subjective score 1–5)
Background noise persistence (SNR measured using professional audio software)
Voice naturalness (distortion, interruption, "robotic" sound)
Scenario 1: City street (continuous traffic + occasional horn sounds)
ENC performance: Suppresses background traffic noise, but each car horn "leaks" in, the other party clearly hears a "beep" sound.
AI performance: Horn sounds almost completely disappear, voice is stable. SNR improved by approximately 8 dB.
Conclusion: AI significantly outperforms, especially in handling sudden high-frequency noise.
Scenario 2: Coffee Shop (Background Music + Multiple Conversations)
ENC Performance: Music was attenuated, but conversations at neighboring tables were still faintly audible, especially when the other party raised their voice.
AI Performance: Background voices were effectively suppressed, with only a very faint reverberation of music remaining. Tester feedback: "The other party thought I was in a quiet office."
Key Detail: The AI model can distinguish between "non-target voices" and "target voices," something ENC cannot do at all.
Scenario 3: Working from Home (Mechanical Keyboard + Air Conditioner + Dog Barking)
ENC Performance: Air conditioner noise was handled well, but keyboard clicking and dog barking were completely penetrating. The other party repeatedly asked, "Are you renovating over there?"
AI Performance: Keyboard sounds were significantly reduced (retaining slight tactile feedback without affecting speech), and dog barking was identified as a "non-speech event" and removed. The only weakness: Occasionally, there were slight interruptions in speech during rapid, continuous typing.
Interesting finding: AI handles "routine noise" (like keyboard noise) worse than "sudden noise"-indicating there's still room for improvement in the training data.
Scenario 4: Quiet Office (Benchmark) Both performed almost identically, with natural, distortion-free speech. This proves that AI did not "overprocess" sound in a clean environment.
Power Consumption and Latency: How Much Does AI Cost?
We monitored battery consumption over 2 hours of continuous calls:
Model E (ENC): 12% power consumption
Model A (AI): 16% power consumption A difference exists, but its impact on mainstream UC headsets (typically offering 15+ hours of battery life) is limited. Regarding latency, the AI model introduces approximately 15–20ms of additional processing time-completely imperceptible in voice calls (human hearing threshold is approximately 30–50ms), but caution is advised in low-latency scenarios (such as live voice-over).
So, how should ordinary users choose?
Choose traditional ENC if you:
Are budget-sensitive (common in large-scale enterprise deployments)
Use in relatively quiet or monotonous environments (e.g., call centers, fixed workstations)
Highly value battery life (e.g., 24/7 field staff)
Choose AI noise cancellation if you:
Frequently make calls in varied and noisy environments (coffee shops, airports, shared offices)
Are you a salesperson, consultant, or remote worker-clear voice = professional image
Are you willing to pay a little more for the embarrassment of "the other party not being able to hear you"
For us, the answer is clear: AI noise cancellation isn't the future, it's already here. As chip costs decrease, we are gradually bringing this technology down to our mid-range product line. After all, in the Zoom era, nobody wanted to miss out on a major client because of background noise.
Finally, let's be honest: there's no absolute good or bad technology, only whether it matches the scenario. As a manufacturer, we don't advocate for "AI omnipotence," but we believe: a good audio experience should make users forget the technology exists-whether you're on the subway, in the kitchen, or in a meeting room, the other party should only hear you, not your everyday noise.
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