Underwater vessel sound recognition based on multi-layer feature and attention mechanism

Area of Science:

• Marine acoustics
• Signal processing
• Artificial intelligence

Background:

• Vessel recognition using hydroacoustic signals is crucial but challenging due to complex marine environments and noise.
• Underwater acoustic signal transmission is affected by environmental variability and interference from sources like waves and marine life.
• Accurate identification of vessel targets is hindered by signal degradation and noise.

Purpose of the Study:

• To propose an advanced deep learning model for robust vessel recognition from hydroacoustic signals.
• To enhance the model's ability to capture contextual information and focus on critical features in noisy conditions.
• To improve the feature representation capability for better performance in complex underwater scenarios.

Main Methods:

• Development of the Emphasized Dimension Attention and Future Fusion-Time Delay Neural Network (EDAFF-TDNN).
• Integration of Squeeze and Excitation Block (SE-Block) for dynamic feature map weight adjustment and contextual information capture.
• Implementation of feature fusion for multi-layer feature extraction and an attention mechanism for focusing on key feature dimensions.

Main Results:

• The EDAFF-TDNN model demonstrated improved performance in capturing contextual information and feature representations.
• The attention mechanism enabled the model to focus on crucial information, enhancing performance in complex underwater scenarios.
• Experiments on the ShipsEar dataset yielded a high recognition accuracy of 98.2% for vessel targets.

Conclusions:

• The proposed EDAFF-TDNN model effectively addresses the challenges of vessel recognition in complex marine environments.
• The combination of emphasized dimension attention and future fusion significantly improves the accuracy and robustness of hydroacoustic signal analysis.
• This research contributes a powerful tool for underwater target identification, with potential applications in maritime surveillance and safety.