Convolutional Neural Networks for Aircraft Noise Monitoring

TL;DR

This paper demonstrates that convolutional neural networks can effectively identify non-aircraft noise events in airport surroundings, achieving high accuracy and aiding noise mitigation efforts.

Contribution

The study introduces a CNN-based system for detecting non-aircraft noise events, with a publicly available dataset and implementation, advancing noise monitoring techniques.

Findings

Achieved 97% accuracy in noise event classification

CNN effectively distinguishes aircraft from non-aircraft noise

Provides open-source data and model for further research

Abstract

Air travel is one of the fastest growing modes of transportation, however, the effects of aircraft noise on populations surrounding airports is hindering its growth. In an effort to study and ultimately mitigate the impact that this noise has, many airports continuously monitor the aircraft noise in their surrounding communities. Noise monitoring and analysis is complicated by the fact that aircraft are not the only source of noise. In this work, we show that a Convolutional Neural Network is well-suited for the task of identifying noise events which are not caused by aircraft. Our system achieves an accuracy of 0.970 when trained on 900 manually labeled noise events. Our training data and a TensorFlow implementation of our model are available at https://github.com/neheller/aircraftnoise.