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

Phase 4: DCL System Using Deep Learning Approaches for Land-Based or Ship-Based Real-Time Recognition and Localization of Marine Mammals - Distributed Processing and Big Data Applications

Approved for public release; distribution is unlimited.

FINAL REPORT DCL System Using Deep Learning Approaches for Land-Based or Ship-Based Real- Time Recognition and Localization of Marine Mammals

Phase 2: DCL System Using Deep Learning Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals - Machine Learning Detection Algorithms

Overarching goals for this work aim to advance the state of the art for detection, classification and localization (DCL) in the field of bioacoustics. This goal is primarily achieved by building a generic framework for detection-classification (DC) using a fast, efficient and scalable architecture, demonstrating the capabilities of this system using on a variety of low-frequency mid-frequency c...

14: DCL System Research Using Advanced Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals

Phase 1: DCL System Research Using Advanced Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals - HPC System Implementation

We aim to investigate advancing the state of the art of detection, classification and localization (DCL) in the field of bioacoustics. The two primary goals are to develop transferable technologies for detection and classification in: (1) the area of advanced algorithms, such as deep learning and other methods; and (2) advanced systems, capable of real-time and archival and processing. This pro...