Low-frequency whale call detection in distributed acoustic sensing data
TL;DR
A dedicated and efficient workflow for creating strong labels from DAS data, building on the DAS4Whales Python package is presented, which is used to strongly label data from Svalbard, Norway, and offshore of Oregon, USA, with fin and blue whale calls, in multiple linked representations that include time, space, and frequency.
Abstract
Distributed acoustic sensing (DAS) adds a spatial component to conventional acoustic recordings, providing positional insights for baleen whale monitoring. Compared to traditional PAM methods, DAS drastically increases data volumes (many TBs/day), requiring real-time analysis solutions. Deep learning methods are particularly well-suited for efficient data analysis of these datasets and ultimately, the generation of actionable data products, but require labeled data for training and evaluation. Here, we present a dedicated and efficient workflow for creating strong labels from DAS data, building on the DAS4Whales Python package. Our process includes: filtering and compressing datasets using sparse matrices in the f–k domain (ranging from 66 to 99% compression); standardizing the data structure and storage format by interpolating datasets to a sampling rate of 200 Hz and an element spacing of 8 m; and a user-interface prototype which allows for fast and intuitive labeling. We use this workflow to strongly label data from Svalbard, Norway, and offshore of Oregon, USA, with fin and blue whale calls, in multiple linked representations that include time, space, and frequency. This dataset is then used to train and evaluate a deep learning model to detect baleen whale calls in DAS data.