Determining the origin of impulsive noise events using paired wireless sound sensors

TL;DR

This paper presents a machine learning-based method using paired wireless sound sensors and on-edge processing to identify the origin of impulsive noise events, achieving over 70% detection accuracy at a shooting range.

Contribution

It introduces a novel approach combining paired sensors, privacy-preserving spectrograms, and CNNs for source identification of impulsive noises in real-world settings.

Findings

70.8% impulsive noise detection rate

90.3% accuracy in predicting noise origin

Effective use of cross-correlation with CNNs

Abstract

This work investigates how to identify the source of impulsive noise events using a pair of wireless noise sensors. One sensor is placed at a known noise source, and another sensor is placed at the noise receiver. Machine learning models receive data from the two sensors and estimate whether a given noise event originates from the known noise source or another source. To avoid privacy issues, the approach uses on-edge preprocessing that converts the sound into privacy compatible spectrograms. The system was evaluated at a shooting range and explosives training facility, using data collected during noise emission testing. The combination of convolutional neural networks with cross-correlation achieved the best results. We created multiple alternative models using different spectrogram representations. The best model detected 70.8\% of the impulsive noise events and correctly predicted 90.3\% of the noise events in the optimal trade-off between recall and precision.