Marine Soundscapes - discriminating and understanding biological and anthropogenic sources of sound from autonomous vehicles

Sound is central to the way marine mammals and many fish species communicate, search for prey, and navigate to avoid predators and hazards. Anthropogenic noise sources may mask these biologically important sounds and can cause physiological and behavioural impacts that may extend over large areas of the marine environment.
One of the new frontier areas of ocean exploration is using fleets of marine autonomous vehicles to collect oceanographic data. Improvements in battery efficiency and computer technology mean that Passive Acoustic Monitoring (PAM) devices can now be carried on these vehicles enabling soundscapes within the oceans to be determined at unprecedented temporal and spatial resolution. These PAM devices record huge volumes of data that require new signal processing methodologies and big data approaches to enable effective real-time data transfer and post-mission analysis. This project will therefore develop machine-learning techniques to discriminate between different sources of sound, using PAM data collected from fleets of marine autonomous vehicles.
As well as improving our understanding of sound in the oceans, the techniques developed in this project are directly relevant to monitoring of natural seabed seeps as well as leaks from industrial infrastructure (pipelines and rigs), and from carbon capture and storage projects.