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Updated: Jun 17, 2025

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

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Adaptive iterative transfer learning for effective snapping shrimp sound detection.

Dawoon Lee¹, Gihoon Byun², Wookeen Chung¹

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

The Journal of the Acoustical Society of America

|August 9, 2024

Summary

This summary is machine-generated.

This study introduces adaptive iterative transfer learning to detect underwater snapping shrimp bioacoustics. This method improves signal detection and reduces false positives in complex marine soundscapes.

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Area of Science:

Marine Bioacoustics
Signal Processing
Machine Learning

Background:

Underwater soundscapes are complex, making the detection of specific bioacoustic signals challenging.
Snapping shrimp are a significant biological sound source, but their signals can be masked by ambient noise.
Existing methods for bioacoustic signal detection often struggle with accuracy in noisy field data.

Purpose of the Study:

To develop and evaluate an adaptive iterative transfer learning approach for detecting snapping shrimp bioacoustics.
To improve the accuracy and reliability of underwater sound signal classification.
To enhance the ability to distinguish snapping shrimp sounds from various forms of ambient noise.

Main Methods:

An adaptive iterative transfer learning network was developed.
The network was initially trained on pre-classified snapping shrimp sounds and Gaussian noise.
Iterative refinement involved using classified ambient noise from field data for further training.

Main Results:

Significant improvements in classification precision and recall were achieved through iterative transfer learning.
The trained network successfully detected signals previously difficult to identify with threshold methods.
False detection rates decreased, and detection probability increased with each iteration.

Conclusions:

Iterative transfer learning enhances the realism of training data by incorporating field noise characteristics.
The proposed network offers a robust solution for detecting challenging bioacoustic signals in underwater environments.
This approach improves the efficacy of bioacoustic monitoring in marine ecosystems.