  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.
