Underwater noise recognition of marine vessels passages: two case studies using hidden Markov models

Abstract

Passive acoustic monitoring (PAM) is emerging as a cost-effective non-intrusive method to monitor the health and biodiversity of marine habitats, including the impacts of anthropogenic noise on marine organisms. When long PAM recordings are to be analysed, automatic recognition and identification processes are invaluable tools to extract the relevant information. We propose a pattern recognition methodology based on hidden Markov models (HMMs) for the detection and recognition of acoustic signals from marine vessels passages and test it in two different regions, the Tagus estuary in Portugal and the Öresund strait in the Baltic Sea. Results show that the combination of HMMs with PAM provides a powerful tool to monitor the presence of marine vessels and discriminate different vessels such as small boats, ferries, and large ships. Improvements to enhance the capability to discriminate different types of small recreational boats are discussed.