Phase 3: DCL System Using Deep Learning Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals - Bioacoustic Applicaitons

TL;DR

This research develops advanced deep learning-based systems for real-time detection and localization of marine mammals using land-based or ship-based acoustic data, enhancing data mining capabilities in large passive acoustic archives.

Contribution

It introduces the HPC-ADA system and DeLMA software, integrating machine learning for improved detection and classification of marine mammals in passive acoustic data.

Findings

Enhanced detection accuracy of marine mammals.

Real-time processing capability for large datasets.

Integration of HPC-ADA with DeLMA for efficient data analysis.

Abstract

Goals of this research phase is to investigate advanced detection and classification pardims useful for data-mining passive large passive acoustic archives. Technical objectives are to develop and refine a High Performance Computing, Acoustic Data Accelerator (HPC-ADA) along with MATLAB based software based on time series acoustic signal Detection cLassification using Machine learning Algorithms, called DeLMA. Data scientists and biologists integrate to use the HPC-ADA and DeLMA technologies to explore data using newly developed techniques aimed at inspection of data extracted at large spatial and temporal scales.