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

Abstract

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 project will focus on long-term, continuous datasets to provide automatic recognition, minimizing human time to annotate the signals. Effort will begin by focusing on several years of multi-channel acoustic data collected in the Stellwagen Bank National Marine Sanctuary (SBNMS) between 2006 and 2010. Our efforts will incorporate existing technologies in the bioacoustics signal processing community, advanced high performance computing (HPC) systems, and new approaches aimed at automatically detecting-classifying and measuring features for species-specific marine mammal sounds within passive acoustic data.

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Document Details

Document Type
Technical Report
Publication Date
Sep 30, 2012
Accession Number
ADA573473

Entities

People

  • Christopher W Clark
  • Peter J. Dugan
  • Sofie M. Van Parijs
  • Yann Le Cun

Organizations

  • Cornell Lab of Ornithology

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Animals
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Biological Sciences
  • Computer Science
  • Computer Vision
  • Computers
  • Deep Learning
  • Detection
  • Detectors
  • High Performance Computing
  • Machine Learning
  • Mammals
  • Marine Mammals
  • Neural Networks
  • Recognition
  • Signal Processing

Readers

  • Acoustical Oceanography.
  • Marine Mammal Biology
  • Parallel and Distributed Computing.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks