HARP North Atlantic beaked whales: Echolocation click collection for machine learning

This dataset package is publicly available to advance the detection of
beaked whale populations through passive acoustic monitoring. This
extensive dataset from the western North Atlantic has been instrumental in
developing a deep neural network (DNN) to improve the detection of
ephemeral events.  The volume of data generated by passive
acoustic monitoring can be overwhelming, complicating efforts to quantify
species occurrence for effective conservation and management. Automation
of data processing using machine learning algorithms enables efficient
species identification using their sounds. Beaked whale acoustic events,
often infrequent and ephemeral, can be missed when co-occurring with
signals of more abundant, and acoustically active species that dominate
acoustic recordings. Large-scale classification efforts using DNN, which
included beaked whales as one of many classes along with other odontocete
species and anthropogenic signals, often missed the ephemeral events in
favor of more common and dominant classes. By training the DNN to focus on
the taxonomic family of beaked whales, we demonstrate that ephemeral
events can be correctly and efficiently identified to species, even with
few echolocation clicks. This classification method can support improved
estimation of beaked whale occurrence in regions of high odontocete
acoustic activity, and this dataset package can be used to develop
classification methods to improve data availability for these rare
species. We kindly ask that if this dataset is used, authors cite this
repository and the accompanying article that provides detailed information
on how the dataset has been collected and processed.