The processed Deepships dataset and ShipsEar dataset.

Underwater acoustic target classification (UATC) aims to identify the type of unknown acoustic sources using passive sonar in oceanic remote sensing scenarios. However, the variability of underwater acoustic environment and the presence of complex background noises create significant obstacles to improving accuracy of UATC. To address these challenges, we develop an innovative deep neural network (DNN) algorithm integrated by  multiscale feature extractor and efficient channel attention mechanism. Firstly, auditory fusion features, including MFCC and GFCC, along with their differential values, are concatenated to represent the amplitude and phase structure information of underwater acoustic signals in time-frequency (TF) domain. Secondly, the integration of multi-scale convolution with an efficient channel attention (ECA) mechanism is introduced to learn and select crucial information from the auditory fusion features. The proposed algorithm efficiently manages and refine the importance of coarse-to-fine representations of acoustic signals, thereby improving the adaptability and reliability in various UATC tasks. Experimental results using the provided datasets have demonstrated that the proposed algorithm significantly outperforms state-of-the-art methods in classification accuracy.