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

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

28 Mar 2024 (modified: 21 Feb 2017)ICLR 2017 workshop submissionReaders: 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
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