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.

本文提出了一种利用神经网络进行水声信号识别的方法，通过从频谱中提取特征，利用深度学习模型不断学习抽象特征并调整权重，以提高分类性能。

基于显著特征的水声信号识别

Underwater Acoustic Signal Recognition Based on Salient Features

The separation of single-channel underwater acoustic signals is a challenging
problem with practical significance. In view of the signal separation problem
with unknown numbers of signals, we propose a solution with a fixed number of
output channels, enabling it to avoid the dimensional disaster caused by the
permutation problem induced by the alignment of outputs to targets.
Specifically, we modify two algorithms developed for known numbers of signals
based on autoencoders, which are highly explainable. We also propose a new
performance evaluation method for situations with mute channels. Experiments
conducted on simulated mixtures of radiated ship noise show that the proposed
solution can achieve similar separation performance to that attained with a
known number of signals. The mute channel output is also good.

本文提出了一种基于自编码器的可解释算法，用于分离未知数量的水声信号，并利用新的性能评估方法处理了静默通道的情况。实验结果表明，该方法在模拟的声纳信号分离方面表现出与已知数量信号分离方法相当的性能。