Innovation Model: Belt-Clip “Smart Hub”

Aurix is built exclusively for low-income communities. Our model reduces cost and stigma by moving expensive processing out of the ear and into a modular belt-clip hub.

How Aurix Works (3 Pillars)

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Decentralized Design

Move the core hardware out of the ear canal into a belt-clip box—dramatically cutting the cost of precision miniaturization.

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AI Adaptive Noise Reduction

Optimized for factories, traffic, and other loud settings—automatically enhancing speech while filtering harmful noise.

Dual-Mode Switching

One tap to switch between hearing-assist mode and Bluetooth earphone mode—work and life, seamlessly connected.

System Architecture (Simplified)

A clear path: capture → enhance speech → output, with a safe power chain.

Audio signal path
Microphone (environment + speech)
AI Noise Reduction (speech-first)
Amplification + Output (earphones)
Power path
Lithium Battery
BMS (protection & stability)
DC-DC / Regulation

Prototype Gallery

Switch Smart Product Prototype

Prototype showcase: Universal 3.5mm interface and tactile control knob for a frictionless user experience.

Switch Smart internal prototype layout

Internal layout: modular wiring and control board design for low-cost maintenance and upgrades.

Why this form factor matters

By shifting complexity into a belt-clip hub, Aurix reduces specialized miniaturization, improves repairability, and keeps the ear-side discreet—key for adoption in low-income communities where both cost and stigma are barriers.

Model Evidence: MFCC Comparison

Visualizing the difference between raw mixed audio and denoised output (environment + speech).

MFCC - Denoised Output (Environment + Speech)

Denoised Output MFCC (Environment + Speech)

MFCC - Raw Mixed Output (Environment + Speech)

Raw Mixed Output MFCC (Environment + Speech)

What to look for
  • • The denoised MFCC shows clearer structure that corresponds to speech features.
  • • The raw mixed MFCC contains more scattered energy from background noise.
  • • This supports our goal: speech-first listening in noisy working environments.