Determining the origin of impulsive noise events using paired wireless sound sensors
This addresses noise source identification in specific environments like shooting ranges, but is incremental as it applies existing machine learning methods to a new application.
The paper tackles the problem of identifying whether impulsive noise events originate from a known source or another source using paired wireless sound sensors, achieving 70.8% detection rate and 90.3% correct prediction rate in optimal trade-off between recall and precision.
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.