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.

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