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Communication Dans Un Congrès Année : 2018

Use of deep features for the automatic classification of fish sounds

Résumé

— 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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Dates et versions

hal-01802551 , version 1 (29-05-2018)

Identifiants

  • HAL Id : hal-01802551 , version 1

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Marielle Malfante, Omar Mohammed, Cedric Gervaise, Mauro Dalla Mura, Jerome I. Mars. Use of deep features for the automatic classification of fish sounds. OCEANS 2018 - OCEANS '18 MTS/IEEE. Ocean Planet – It’s our home., May 2018, Kobe, Japan. ⟨hal-01802551⟩
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