The Next Upgrade for Sleep Monitors: Snore Detection and Smart Anti-Snore

Adding on-device snore detection with real-time anti-snore feedback transforms a passive sleep tracker into an active sleep improvement tool.

The Snoring Problem

~45% of adults snore occasionally, 25% are habitual snorers. Snoring can indicate simple snoring, UARS, or sleep apnea.

On-Device Sound Recognition

A dedicated model running on a smart pillow, bedside monitor, or CPAP machine fills the gap between smartphone apps (privacy concerns) and wearables (indirect detection).

Detection Pipeline

Audio capture → MFCC feature extraction → Snore classification (5-layer CNN) → Severity assessment (mild/moderate/severe) → Intervention trigger (airbag, haptic, sound).

Technical Specs

Model: 5-layer depthwise CNN, 85KB INT8. Inference: 3-5ms per 2s window on Cortex-M4. RAM: 1.2MB. Accuracy: 94% snore detection.

Product Applications

Smart pillow, bedside monitor, CPAP integration. All use the same core SDK with different intervention mechanisms.

Conclusion

Snore detection is the next logical upgrade for sleep monitors. Our SDK includes a pre-trained model for Cortex-M class MCUs with no cloud dependency.