About this Research Topic
Unidentified underwater sounds are not commonly a project priority and so are rarely reported, often remaining an elusive, yet valuable source of acoustic information. This knowledge gap can be alleviated through open access sharing and reporting of their characteristics and spatiotemporal distribution, together with potential methods to identify the sources and behaviors are associated with the sound. The aim of this Research Topic is to provide a focus area where information about such signals can be shared for future comparison. This topic is supported by the International Quiet Ocean Experiment, via the Global Library of Underwater Biological Sounds.
This Research Topic calls for papers that:
• Develop detection algorithms with a focus on unidentified underwater biological sounds;
• Quantitatively characterize spatiotemporal patterns in individual or mass phenomena unidentified sounds;
• Compare characteristics of unidentified sounds to similar known sounds, to assist source identification;
• Examples of where recordings of sounds from known sources exhibit atypical temporal or spectral characteristics, resulting from behavioral change or propagation conditions, and how this may affect detection performance; and
• Techniques to validate sound sources.
This topic is supported by the International Quiet Ocean Experiment, via the Global Library of Underwater Biological Sounds. We acknowledge the partial funding of the manuscripts published in this Research Topic by Université de Perpignan provided through a Richard Lounsbury Foundation grant. We hereby state publicly that neither Université de Perpignan or the Richard Lounsbery Foundation has had any editorial input in articles included in this Research Topic, thus ensuring that all aspects of this Research Topic are evaluated objectively, unbiased by any specific policy or opinion of Université de Perpignan or the Richard Lounsbery Foundation.
Keywords: Artificial intelligence, deep learning, mass phenomena, ecological modelling, unidentified sounds.
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