Comparison of machine learning techniques for the classification of echolocation clicks from three species of odontocetes

Auteurs-es

  • Marie A. Roch Dept. of Computer Science, San Diego State University, 5500 Campanile Dr., San Diego, CA 92182-7720, United States
  • Melissa S. Soldevilla Scripps Institution of Oceanography, University of California at San Diego, San Diego, CA 92093-0205, United States
  • Rhonda Hoenigman Dept. of Computer Science, San Diego State University, 5500 Campanile Dr., San Diego, CA 92182-7720, United States
  • Sean M. Wiggins Scripps Institution of Oceanography, University of California at San Diego, San Diego, CA 92093-0205, United States
  • John A. Hildebrand Scripps Institution of Oceanography, University of California at San Diego, San Diego, CA 92093-0205, United States

Mots-clés :

Artificial intelligence, Classification (of information), Communication channels (information theory), Computer networks, Database systems, Dolphins (structures), Error analysis, Error detection, Face recognition, Image retrieval, Learning algorithms, Learning systems, Magnetostrictive devices, Time series analysis, Cepstral analysis, Echolocation clicks, Feature vectors, Odontocetes, Teager energy operator

Résumé

Ce travail concerne la création d’un système pour identifier trois espèces d’odontocètes par les clics d’écholocation: la baleine à bec de Blainville, la baleine pilote, et le dauphin de Risso. Les clics sont identifiés par l’opérateur d’énergie Teager-Kaiser, et les vecteurs cepstraux sont construits. Dans un travail de détection, on compare les résultats obtenus avec deux modèles différents : le modèle de mélange gaussiens (MMG) et la machine à vecteurs de support (MVS). Les résultats de la détection sont exprimés par les courbes de DET, « Detection Error Tradeoff». Le point sur les courbes de DET où les probabilités de fausses alarmes et manques de détection sont égales est comme suit : la baleine à bec de Blainville (MMG 3,32%/MVS 5,54%), la baleine pilote (MMG 16,18%/MVS 15,00%) et le dauphin de Risso (MMG 0,03%/MVS 0,70%).

Fichiers supplémentaires

Publié-e

2008-03-01

Comment citer

1.
Roch MA, Soldevilla MS, Hoenigman R, Wiggins SM, Hildebrand JA. Comparison of machine learning techniques for the classification of echolocation clicks from three species of odontocetes. Canadian Acoustics [Internet]. 1 mars 2008 [cité 19 févr. 2025];36(1):41-7. Disponible à: https://jcaa.caa-aca.ca/index.php/jcaa/article/view/1989

Numéro

Vol. 36 No. 1 (2008)

Rubrique

Actes du congrès de la Semaine canadienne d'acoustique

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