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

arXiv: https://arxiv.org/abs/2108.11758 · HTML: https://mlsanity.com/paper/2108.11758/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 23 Aug 2021.

## Builds on (most cited first, 2 of 2)

- [Sound Event Detection in Synthetic Audio: Analysis of the DCASE 2016 Task Results](https://mlsanity.com/paper/1711.05551/) (2017)
- [Automatic Detection Of Noise Events at Shooting Range Using Machine Learning](https://mlsanity.com/paper/2107.11453/) (2021)

## Abstract (arXiv)

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.