Clairaudience: a lightweight attentional residual neural network with data augmentation and feature fusion for underwater acoustic target recognition

Abstract Marine engineering has boomed and many deep learning-based methods have been proposed for underwater acoustic target recognition. However, most of these methods are dedicated to develop more complex convolutional neural networks to achieve better performance. This results in these models being unable to be deployed to low cost and miniaturized automatic underwater vehicles. A novel lightweight attentional residual neural network with data augmentation and feature fusion is proposed in this paper. Mel Frequency Cepstral Coefficient (MFCC), delta-MFCC and delta–delta MFCC features are extracted in the time dimension for fusion to obtain the fusion feature. The SpecAugment data augmentation is also used to enhance the randomness and diversity of features by masking in time and frequency dimensions randomly. Shuffle attention in the residual blocks is introduced to enhance the representation of features. The lightweight model is evaluated and compared by using several metrics on ShipsEar and DeepShip datasets. The proposed lightweight model only requires 1.628 M parameters for the trained model. This work shows that the proposed method requires small memory storage, while it achieved comparative performance.