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

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...

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Bibliographic Details
Main Authors: Dorian, Cazau, Lefort, Riwal, Bonnel, Julien, Zarader, Jean-Luc, Adam, Olivier
Format: Report
Language:unknown
Published: arXiv 2017
Subjects:
Machine Learning stat.ML
Machine Learning cs.LG
Sound cs.SD
FOS Computer and information sciences
Humpback Whale
Online Access:https://dx.doi.org/10.48550/arxiv.1703.10887
https://arxiv.org/abs/1703.10887
Description
Summary: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. : arXiv admin note: text overlap with arXiv:1702.02741 by other authors

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