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Related Experiment Video

Updated: May 31, 2026

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

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Deep Learning Based Underwater Acoustic Target Recognition: Introduce a Recent Temporal 2D Modeling Method.

Jun Tang¹, Wenbo Gao¹, Enxue Ma¹

¹School of Civil Engineering, Tianjin University, Tianjin 300072, China.

Sensors (Basel, Switzerland)

|March 13, 2024

Summary

This summary is machine-generated.

A new deep learning approach combines 1D and 2D models for ship radiation noise classification. This method improves accuracy and reduces model parameters, offering an efficient alternative for underwater target recognition.

Keywords:

deep learning short-time Fourier transform temporal 2D modeling underwater acoustic target recognition

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Area of Science:

Signal Processing
Deep Learning
Underwater Acoustics

Background:

Deep learning models are increasingly used for underwater target recognition.
Current models typically use either 1D (time-domain) or 2D (time-frequency) processing, each with limitations.

Purpose of the Study:

To introduce and evaluate a novel temporal 2D modeling method for ship radiation noise classification.
To combine 1D and 2D deep learning approaches for improved underwater target recognition.

Main Methods:

A temporal 2D modeling method was adapted for ship radiation noise classification.
This method leverages the periodic characteristics of time-domain signals, converting them into 2D signals.
2D convolution was used to identify long-term correlations, overcoming 1D convolution limitations. Models were trained and tested on the Deepship database.

Main Results:

The combined 1D and 2D method improved classification accuracy by 0.9% and reduced parameter count by 30%.
Models trained on time-domain signals showed greater sensitivity and a 30% smaller storage footprint.
Models trained on time-frequency representations achieved 1-2% higher accuracy.

Conclusions:

The proposed temporal 2D modeling method offers a new, effective option for constructing and optimizing deep learning models for underwater target recognition.
A trade-off exists between model size/sensitivity (time-domain) and accuracy (time-frequency), allowing for tailored model selection.