skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

Detecting, classifying, and counting blue whale calls with Siamese neural networks

The Journal of the Acoustical Society of America, 2021-05, Vol.149 (5), p.3086-3094 [Peer Reviewed Journal]

Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0001-4966 ;EISSN: 1520-8524 ;DOI: 10.1121/10.0004828

Full text available

Citations Cited by
  • Title:
    Detecting, classifying, and counting blue whale calls with Siamese neural networks
  • Author: Zhong, Ming ; Torterotot, Maelle ; Branch, Trevor A. ; Stafford, Kathleen M. ; Royer, Jean-Yves ; Dodhia, Rahul ; Lavista Ferres, Juan
  • Subjects: Environmental Sciences
  • Is Part Of: The Journal of the Acoustical Society of America, 2021-05, Vol.149 (5), p.3086-3094
  • Description: The goal of this project is to use acoustic signatures to detect, classify, and count the calls of four acoustic populations of blue whales so that, ultimately, the conservation status of each population can be better assessed. We used manual annotations from 350 h of audio recordings from the underwater hydrophones in the Indian Ocean to build a deep learning model to detect, classify, and count the calls from four acoustic song types. The method we used was Siamese neural networks (SNN), a class of neural network architectures that are used to find the similarity of the inputs by comparing their feature vectors, finding that they outperformed the more widely used convolutional neural networks (CNN). Specifically, the SNN outperform a CNN with 2% accuracy improvement in population classification and 1.7%–6.4% accuracy improvement in call count estimation for each blue whale population. In addition, even though we treat the call count estimation problem as a classification task and encode the number of calls in each spectrogram as a categorical variable, SNN surprisingly learned the ordinal relationship among them. SNN are robust and are shown here to be an effective way to automatically mine large acoustic datasets for blue whale calls.
  • Publisher: Acoustical Society of America
  • Language: English
  • Identifier: ISSN: 0001-4966
    EISSN: 1520-8524
    DOI: 10.1121/10.0004828
  • Source: Alma/SFX Local Collection
Back to results list
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait