Use of deep features for the automatic classification of fish sounds

Marielle Malfante 1 Omar Mohammed 2 Cedric Gervaise 3 Mauro Dalla Mura 1 Jerome Mars 1
1 GIPSA-SIGMAPHY - SIGMAPHY
GIPSA-DIS - Département Images et Signal
2 GIPSA-CRISSP - CRISSP
GIPSA-DPC - Département Parole et Cognition
Abstract : — The work presented in this paper focuses on the environmental monitoring of underwater areas using acoustic signals. In particular, we propose to compare the effectiveness of various feature sets used to represent the underwater acoustic data for the automatic processing of fish sounds We focus on the detection and classification tasks. Specifically, we compare the use of features issued from signal processing presented and validated in [15], [16] to the use of features obtained through deep convolutional neural networks. Experimental results show that the use of signal processing features outperform the deep features in terms of classification accuracy.
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Contributeur : Jerome Mars <>
Soumis le : mardi 29 mai 2018 - 14:29:39
Dernière modification le : jeudi 11 octobre 2018 - 20:02:25
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  • HAL Id : hal-01802551, version 1

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Marielle Malfante, Omar Mohammed, Cedric Gervaise, Mauro Dalla Mura, Jerome Mars. Use of deep features for the automatic classification of fish sounds. OCEANS'18 MTS/IEEE, May 2018, Kobe, Japan. 〈hal-01802551〉

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