Underwater Acoustic Target Recognition based on Smoothness-inducing Regularization and Spectrogram-based Data Augmentation

arXiv:2306.06945v2 Announce Type: replace-cross
Abstract: 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, …

abstract advancement arxiv augmentation availability capacity cs.lg cs.sd data environments hinder modeling recognition regularization spectrogram support systems type underwater