Underwater Acoustic Signal Recognition Based on Salient Feature

TL;DR

This paper introduces a deep learning-based method for underwater acoustic signal recognition that automatically learns features from spectral data, overcoming limitations of traditional rule-based systems.

Contribution

It proposes a neural network approach utilizing continual learning to improve classification of underwater acoustic signals from spectral features.

Findings

Deep learning models automatically extract abstract features.

Continual learning enhances classification accuracy.

Outperforms traditional spectral analysis methods.

Abstract

With the rapid advancement of technology, the recognition of underwater acoustic signals in complex environments has become increasingly crucial. Currently, mainstream underwater acoustic signal recognition relies primarily on time-frequency analysis to extract spectral features, finding widespread applications in the field. However, existing recognition methods heavily depend on expert systems, facing limitations such as restricted knowledge bases and challenges in handling complex relationships. These limitations stem from the complexity and maintenance difficulties associated with rules or inference engines. Recognizing the potential advantages of deep learning in handling intricate relationships, this paper proposes a method utilizing neural networks for underwater acoustic signal recognition. The proposed approach involves continual learning of features extracted from spectra for the classification of underwater acoustic signals. Deep learning models can automatically learn abstract features from data and continually adjust weights during training to enhance classification performance.