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

Cazau Dorian, Riwal Lefort, Julien Bonnel, Jean-Luc Zarader, Olivier Adam

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.

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