02054nas a2200169   4500008004100000245010200041210006900143260001600212490000600228520148700234100002301721700002701744700002101771700001601792700002201808856005401830       2019                        eng d00aDeep Machine Learning Techniques for the Detection and Classification of Sperm Whale Bioacoustics0 aDeep Machine Learning Techniques for the Detection and Classific  cJan-12-20190 v93 a<p>We implemented Machine Learning (ML) techniques to advance the study of sperm whale (Physeter macrocephalus) bioacoustics. This entailed employing Convolutional Neural Networks (CNNs) to construct an echolocation click detector designed to classify spectrograms generated from sperm whale acoustic data according to the presence or absence of a click. The click detector achieved 99.5% accuracy in classifying 650 spectrograms. The successful application of CNNs to clicks reveals the potential of future studies to train CNN-based architectures to extract finer-scale details from cetacean spectrograms. Long short-term memory and gated recurrent unit recurrent neural networks were trained to perform classification tasks, including (1) &ldquo;coda type classification&rdquo; where we obtained 97.5% accuracy in categorizing 23 coda types from a Dominica dataset containing 8,719 codas and 93.6% accuracy in categorizing 43 coda types from an Eastern Tropical Pacific (ETP) dataset with 16,995 codas; (2) &ldquo;vocal clan classification&rdquo; where we obtained 95.3% accuracy for two clan classes from Dominica and 93.1% for four ETP clan types; and (3) &ldquo;individual whale identification&rdquo; where we obtained 99.4% accuracy using two Dominica sperm whales. These results demonstrate the feasibility of applying ML to sperm whale bioacoustics and establish the validity of constructing neural networks to learn meaningful representations of whale vocalizations.</p>
1 aBermant, Peter, C.1 aBronstein, Michael, M.1 aWood, Robert, J.1 aGero, Shane1 aGruber, David, F.  uhttp://www.nature.com/articles/s41598-019-48909-4