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2019-03-05 - Colloque/Présentation - poster - Anglais - 1 page(s)

Florentin Juliette , Verlinden Olivier , Dutoit Thierry , Laraba Sohaib , "Recognition of woodpecker calls using a convolutional deep neural network" in Mardi des Chercheurs 2019 (MdC2019), Mons, Belgique, 2019

  • Codes CREF : Sciences de l'ingénieur (DI2000), Intelligence artificielle (DI1180), Ornithologie (DI3166), Acoustique (DI1264)
  • Unités de recherche UMONS : Mécanique rationnelle, Dynamique et Vibrations (F703)
  • Instituts UMONS : Institut des Biosciences (Biosciences)
Texte intégral :

Abstract(s) :

(Anglais) Woodpeckers calls are readily recognizable on spectrograms and this opens the door for their identification from images through a convolutional Deep Neural Network (DNN). We built a dataset of 12154 images, half woodpecker calls (9 classes) and half noise, from Xeno-Canto and private recordings. We experimented with two approaches: a) training a small net (2 convolutional layers, 2 dense layers) from scratch using the theano framework and b) re-training legacy image nets (over 150 layers) using Pytorch. The larger nets successfully differentiate the calls from noise and identify them with an accuracy greater than 90%.