Applications of semi-supervised learning methods in underwater acoustics

Abstract

Research on supervised deep learning methods have shown potential for ocean acoustic applications. Supervised methods require labeled data samples for training. However, ocean acoustics data often do not contain labels. In experimental situations it is possible to estimate source range and speed labels as inferred by GPS data, but the environmental labels may not be easily defined. Applications of machine learning in ocean acoustics will be greatly enhanced by developing ways to utilize semi-supervised learning. In other applications, semi-supervised learning has been accomplished via contrastive learning or self-attention methods. Our work applies semi-supervised learning by developing a contrastive learning framework for acoustic data. First the model does self-training using an augmentation policy on unlabeled data to initialize representations of key features in the data. Then a small set of cleanly labelled samples are given to a model for supervised learning to teach the desired prediction task. Our work in contrastive learning will be applied to transiting surface ship spectrograms to demonstrate the effectiveness for predicting source labels and seabed type. Preliminary work will be presented to illustrate the advantages of semi-supervised learning. [Work supported by ONR contract N00014-19-C-20001.]