Using High Performance Computing to Explore Large Complex Bioacoustic Soundscapes: Case Study for Right Whale Acoustics

This paper describes ongoing work being done at Cornell University to investigate the development of a complex system designed for extracting information from large acoustic datasets. The system, called DeLMA is based on integrating advanced machine learning with high performance computing (HPC). The goal of this work is to provide the capability to accurately detect and classify whale sounds in large acoustics datasets collected using underwater sensors. The case study for this work is focused on detecting the acoustic communication signals of the North Atlantic Right Whale, Eubalaena glacialis, and uses data collected in the Stellwagen Bank National Marine Sanctuary (SBNMS), USA. A summary of the work done for developing a complex detection-classification system and brief description of several algorithms that are used for classifying whale sounds will be covered. A brief discussion on how standard detection algorithms can be incorporated, with no special modifications, into the HPC system for analysis will be mentioned, and two new right whale detection methods are presented, based on continuous region analysis (CRA) and histogram of oriented gradients (HOG). This paper presents a first-hand look at applying the DeLMA system and these algorithms on a large dataset containing over 60,000 channel-hours of acoustic data from the SBNMS. Results from these new detection methods are compared against Baseline algorithms. With the development of the DeLMA system, sound archives can now be explored using a powerful distributed processing architecture. This advancement will allow for rapid execution and visualization of the data using seasonal graphs called diel plots, which show the distribution of detections on a time-of-day vs. time-of-year plane. Diel plots of Baseline, CRA and HOG algorithm results reveal various large-scale features of the seasonality of whale calling behavior. Results are summarized and the authors discuss future areas for study, especially those relate to handling other big passive acoustic data projects.