  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.
