Bi-class classification of humpback whale sound units against complex background noise with Deep Convolution Neural Network

Cazau D., Lefort R., Bonnel, J., Krywyk, J., Zarader JL, Adam, O.

Feb 17, 2017 (modified: Feb 21, 2017) ICLR 2017 workshop submission readers: everyone
  • Abstract: Automatically detecting sound units of humpback whales in complex time-varying background noises is a current challenge for scientists. In this paper, we explore the applicability of Convolution Neural Network (CNN) method for this task. In the evaluation stage, we present 6 bi-class classification experimentations of whale sound detection against different background noise types (e.g., rain, wind). In comparison to classical FFT-based representation like spectrograms, we showed that the use of image-based pretrained CNN features brought higher performance to classify whale sounds and background noise.
  • Conflicts: upmc
  • Keywords: Natural language processing, Deep learning, Applications