Adaptive ship-radiated noise recognition with learnable fine-grained wavelet transform
This work addresses the challenge of accurate ship recognition in noisy underwater settings, which is incremental as it builds on existing recognition systems with adaptive parameter learning.
The paper tackled the problem of ship-radiated noise recognition in variable underwater environments by proposing AGNet, which adapts wavelet parameters to learn acoustic characteristics, resulting in outperforming baseline methods on multiple datasets and showing robust performance against interference.
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.