  Neural networks were used to classify infrasound data. Two different
approaches were compared. One based on the direct classification of time series
data, using a custom implementation of the InceptionTime network. For the other
approach, we generated 2D images of the wavelet transformation of the signals,
which were subsequently classified using a ResNet implementation. Choosing
appropriate hyperparameter settings, both achieve a classification accuracy of
above 90 %, with the direct approach reaching 95.2 %.
