Convolutional Neural Networks for Aircraft Noise Monitoring
This addresses noise monitoring challenges for airports and surrounding communities, but it is incremental as it applies an existing method to a specific domain.
The paper tackled the problem of distinguishing aircraft noise from other noise sources in airport monitoring by using a Convolutional Neural Network, achieving an accuracy of 0.970 on a dataset of 900 manually labeled events.
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.