Adaptive iterative transfer learning for effective snapping shrimp sound detection

Author:

Lee Dawoon1ORCID,Byun Gihoon2,Chung Wookeen1ORCID

Affiliation:

1. Energy and Resources Engineering, National Korea Maritime and Ocean University 1 , Busan 49112, South Korea

2. Convergence Study on the Ocean Science and Technology, Korea Maritime and Ocean University 2 , Busan 49112, South Korea

Abstract

This study aims to detect the bioacoustics signal in the underwater soundscape, specifically those produced by snapping shrimp, using adaptive iterative transfer learning. The proposed network is initially trained with pre-classified snapping shrimp sounds and Gaussian noise, then applied to classify and remove snapping-free noise from field data. This separated ambient noise is subsequently used for transfer learning. This process was iterated to distinguish more effectively between ambient noise and snapping shrimp sounds characteristics, resulting in improved classification. Through iterative transfer learning, significant improvements in precision and recall were observed. The application to field data confirmed that the trained network could detect signals that were difficult to identify using existing threshold classification methods. Furthermore, it was found that the rate of false detection decreased, and detection probability improved with each stage. This research demonstrates that incorporating the noise characteristics of field data into the trained network via iterative transfer learning can generate more realistic training data. The proposed network can successfully detect signals that are challenging to identify using existing threshold classification methods.

Funder

Ministry of Oceans and Fisheries

Ministry of Education

Publisher

Acoustical Society of America (ASA)

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