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arXiv · 1703.10887

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
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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.

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Cazau Dorian, Riwal Lefort, Julien Bonnel, Jean-Luc Zarader, Olivier Adam. 2017-03-31. Bi-class classification of humpback whale sound units against complex background noise with Deep Convolution Neural Network. https://arxiv.org/abs/1703.10887

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