Detecting, classifying, and counting blue whale calls with Siamese neural networks - Université de Bretagne Occidentale
Journal Articles Journal of the Acoustical Society of America Year : 2021

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

Ming Zhong
  • Function : Author
Maëlle Torterotot
Trevor Branch
  • Function : Author
Kathleen Stafford
  • Function : Author
Jean-Yves Royer
Rahul Dodhia
  • Function : Author
Juan Lavista Ferres
  • Function : Author

Abstract

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.

Dates and versions

hal-03263839 , version 1 (17-06-2021)

Identifiers

Cite

Ming Zhong, Maëlle Torterotot, Trevor Branch, Kathleen Stafford, Jean-Yves Royer, et al.. Detecting, classifying, and counting blue whale calls with Siamese neural networks. Journal of the Acoustical Society of America, 2021, 149 (5), pp.3086-3094. ⟨10.1121/10.0004828⟩. ⟨hal-03263839⟩
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