Bi-class classification of humpback whale sound units against complex background noise with Deep Convolution Neural Network
This work addresses a specific challenge for scientists in marine bioacoustics by improving detection accuracy in noisy environments, though it is incremental as it adapts existing CNN techniques to a new domain.
The paper tackled the problem of automatically detecting humpback whale sound units in complex background noise by applying a Convolutional Neural Network (CNN) with image-based pretrained features, achieving higher performance compared to classical spectrogram methods.
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.