Leveraging Unlabelled Data through Semi-supervised Learning to Improve the Performance of a Marine Mammal Classification System

A considerable proportion of the passive acoustic data sets collected for marine mammal conservation purposes remain unanalyzed by human experts. In some cases, the aforementioned proportion amounts to as much as 97% of the entire data set. As a result, research and development into automated classification systems rely on sparsely annotated data sets. In this work we adapt a semi-supervised deep learning approach to develop a classification system of marine mammal vocalizations such that both the annotated and non-annotated portions of an acoustic data set can be used during training.