  Analyzing the ocean acoustic environment is a tricky task. Background noise
and variable channel transmission environment make it complicated to implement
accurate ship-radiated noise recognition. Existing recognition systems are weak
in addressing the variable underwater environment, thus leading to
disappointing performance in practical application. In order to keep the
recognition system robust in various underwater environments, this work
proposes an adaptive generalized recognition system - AGNet (Adaptive
Generalized Network). By converting fixed wavelet parameters into fine-grained
learnable parameters, AGNet learns the characteristics of underwater sound at
different frequencies. Its flexible and fine-grained design is conducive to
capturing more background acoustic information (e.g., background noise,
underwater transmission channel). To utilize the implicit information in
wavelet spectrograms, AGNet adopts the convolutional neural network with
parallel convolution attention modules as the classifier. Experiments reveal
that our AGNet outperforms all baseline methods on several underwater acoustic
datasets, and AGNet could benefit more from transfer learning. Moreover, AGNet
shows robust performance against various interference factors.
