  Underwater acoustic target recognition is a challenging task owing to the
intricate underwater environments and limited data availability. Insufficient
data can hinder the ability of recognition systems to support complex modeling,
thus impeding their advancement. To improve the generalization capacity of
recognition models, techniques such as data augmentation have been employed to
simulate underwater signals and diversify data distribution. However, the
complexity of underwater environments can cause the simulated signals to
deviate from real scenarios, resulting in biased models that are misguided by
non-true data. In this study, we propose two strategies to enhance the
generalization ability of models in the case of limited data while avoiding the
risk of performance degradation. First, as an alternative to traditional data
augmentation, we utilize smoothness-inducing regularization, which only
incorporates simulated signals in the regularization term. Additionally, we
propose a specialized spectrogram-based data augmentation strategy, namely
local masking and replicating (LMR), to capture inter-class relationships. Our
experiments and visualization analysis demonstrate the superiority of our
proposed strategies.

Sound;Machine Learning