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Automatically detecting sound units of humpback whales in complextime-varying background noises is a current challenge for scientists. In thispaper, we explore the applicability of Convolution Neural Network (CNN) methodfor this task. In the evaluation stage, we present 6 bi-class classificationexperimentations of whale sound detection against different background noisetypes (e.g., rain, wind). In comparison to classical FFT-based representationlike spectrograms, we showed that the use of image-based pretrained CNNfeatures brought higher performance to classify whale sounds and backgroundnoise.

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