Unsupervised Learning (Clustering) of Odontocete Echolocation Clicks

Abstract

The primary long-term goal is to develop methods for clustering of marine mammal echolocation clicks to learn about species assemblages where little or no prior knowledge exists about these species signal repertoire. Being able to monitor individual species can be effective, for understanding how certain species use a region and habitat or how naval operations may affect their behavior.

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Document Details

Document Type
Technical Report
Publication Date
Sep 30, 2015
Accession Number
AD1014296

Entities

People

  • Margareta Ackerman
  • Marie A. Roch
  • Simone Baumann-Pickering

Organizations

  • San Diego State University

Tags

DTIC Thesaurus Topics

  • Acoustic Detection
  • Biosonar
  • Cetaceans
  • Clustering
  • Computer Science
  • Detection
  • Electronic Mail
  • Feature Extraction
  • Habitats
  • Mammals
  • Marine Mammals
  • Naval Operations
  • Odontocetes
  • Probabilistic Models
  • Signal Processing
  • Test And Evaluation
  • Unsupervised Machine Learning

Fields of Study

  • Environmental science

Readers

  • Distributed Systems and Data Platform Development
  • Marine Mammal Biology

Technology Areas

  • AI & ML