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

Abstract

The ONR DCL grant focuses on advancing the state of the art for bioacoustic signal detection and classification through researching new technologies, algorithms and systems. This work engages a unique team of experts from Cornell University (CU), New York University (NYU) and Northeast Fisheries Science Center (NEFSC). Aimed at developing new methods and practices for advancing detection classification (DC), the grant team also maintains a higher level goal: addressing general data mining strategies as applied to large, complex acoustic datasets. The underlying focus of the team s work is to integrate and develop new technologies for hardware and software tools based on high performance computing, and reduce these to practice through outreach into the broader bioacoustic community.

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Document Details

Document Type
Technical Report
Publication Date
Sep 30, 2014
Accession Number
ADA617980

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
  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Air Guns
  • Algorithms
  • Artificial Intelligence Software
  • Bayesian Networks
  • Big Data
  • Computer Science
  • Computers
  • Data Mining
  • Deep Learning
  • High Performance Computing
  • Learning
  • Machine Learning
  • Marine Mammals
  • Mobile Phones
  • Neural Networks
  • New York
  • Recognition

Fields of Study

  • Computer science

Readers

  • Acoustical Oceanography.
  • Research Science/Academic Research
  • Software Engineering.

Technology Areas

  • AI & ML