An enhanced system for the detection and active cancellation of snoring signals

TL;DR

This paper presents an advanced system combining neural networks and subband techniques for detecting and actively canceling snoring sounds, improving noise control in social and marital contexts.

Contribution

It introduces a novel system integrating convolutional recurrent neural networks with delayless subband methods for enhanced snoring detection and cancellation.

Findings

Detection stage improves cancellation performance

System tested with real snoring signals

Preliminary detection enhances overall effectiveness

Abstract

Snoring is a common disorder that affects people's social and marital lives. The annoyance caused by snoring can be partially solved with active noise control systems. In this context, the present work aims at introducing an enhanced system based on the use of a convolutional recurrent neural network for snoring activity detection and a delayless subband approach for active snoring cancellation. Thanks to several experiments conducted using real snoring signals, this work shows that the active snoring cancellation system achieves better performance when the snoring activity detection stage is turned on, demonstrating the beneficial effect of a preliminary snoring detection stage in the perspective of snoring cancellation.