Abstract:Soundscape analyses provide an integrative approach to studying the presence and complexity of sounds within long-term acoustic data sets. Acoustic metrics (AMs) have been used extensively to describe terrestrial habitats but have had mixed success in the marine environment. Novel approaches are needed to be able to deal with the added noise and complexity of these underwater systems. Here we further develop a promising approach that applies AM with supervised machine learning to understanding the presence and… Show more
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