Improving The Robustness Of Right Whale Detection In Noisy Conditions Using Denoising Autoencoders And Augmented Training

The aim of this paper is to examine denoising autoencoders (DAEs) for improving the detection of right whales recorded in harsh marine environments. Passive acoustic recordings are taken from autonomous surface vehicles (ASVs) and are subject to noise from sources such as shipping and offshore construction. To mitigate the noise we apply DAEs and consider how best to train the classifier by augmenting clean training data with examples contaminated by noise. Evaluations find that the DAE improves detection accuracy and is particularly effective when the classifier is trained on data that has itself been denoised rather than using a clean model. Further, testing on unseen noises is also effective particularly for noises that exhibit similar character to noises seen in training.