A convolutional neural network applied to measured time series for source range and ocean seabed classification

Source localization and environmental inference are common problems in ocean acoustics requiring computationally intensive algorithms and knowledge of the search space. Convolutional neural networks (CNNs) learn useful features for making predictions directly from a gridded input signal circumventing the costly practice of selecting features or comparisons to a forward propagation model. To take advantage of these benefits, a CNN was trained and...