# How AI Cleans Up Your Voice in Modern Earbuds

> Call quality in earbuds is now determined by AI-powered DSP that separates human speech from background noise in real time.

**URL:** https://www.ciptadusa.com/blog/ai-powered-voice-cleanup-earbuds-20260830  
**Type:** blog  
**Author:** PT Cipta Dua Saudara  
**Category:** Engineering  
**Published:** 2026-08-30  
**Cover:** https://cdn-uagents.enitip.com/uploads/blog/2026-08/daily-engineering-20260830-014606.jpg  

## Article

We tend to think of earbuds as one-way audio devices: a music player. But the latest review of the Soundcore Liberty 5 Pro — crowned "Master of Phone Calls" by its reviewer — reveals that the biggest engineering battle is not in its 11mm drivers, but in the microphone array, DSP, and AI models that clean our voice for the person on the other end of the call. This is the engineering story usually hidden behind the "noise cancellation" label.

## Summary

Call quality in earbuds is now determined by AI-powered DSP that separates human speech from background noise in real time.

## Background

Phone calls through earbuds are now a work staple. The enemy is not volume but noise: wind, traffic, café machines, a mechanical keyboard next door. Manufacturers chase two conflicting metrics — microphones capture everything so the voice sounds dry and natural, while noise needs to be discarded. Separating the two demands computation at very low latency, because delays beyond a few dozen milliseconds make a conversation feel broken.

## The Challenge

The problem is real-time classification. The speaker-separation models used in recording studios need huge compute and can run offline with no time limit. Earbuds have only a few milliwatts and must decide in milliseconds whether an audio segment is "the user's voice" or "interference". This forces engineers to port neural models onto tiny DSPs, then share resources between active noise cancellation (ANC), EQ, and call processing all at once. That balance is delicate: too aggressive at removing noise and the voice sounds robotic; too lenient and the listener struggles to hear.

## Approach

Modern approaches use multi-element microphone arrays working as beamforming — physically attenuating sound from any direction other than the user's mouth — combined with neural networks for non-stationary noise like traffic that does not come from one fixed direction. The same techniques power video conferencing and enterprise headsets. What makes consumer products interesting is executing it in a small, cheap, low-power package. For practitioners the pattern is familiar: the model must be quantized, pruned, and mapped onto specialized accelerators to run in real time.

## Implications

The "Master of Phone Calls" push signals an industry direction: transmit-path voice quality is the new competitive field now that receive-path quality has plateaued. For engineering teams it is a lesson in optimization under hard power budgets — efficient compute becomes a product differentiator, not a footnote. And Indonesia, with its large mobile-first market, is an ideal environment to test call quality against difficult street noise.

## References

- [Soundcore Liberty 5 Pro Review: Master of Phone Calls (Wired)](https://www.wired.com/review/soundcore-liberty-5-pro/)
- [Efficient neural speech separation (IEEE)](https://ieeexplore.ieee.org/document/9054274)
- [Beamforming microphone array primer](https://www.beyondlogic.org/audio/beamforming/)

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*Markdown version of https://www.ciptadusa.com/blog/ai-powered-voice-cleanup-earbuds-20260830 — generated for AI agents and LLM crawlers.*