  Facing the complex marine environment, it is extremely challenging to conduct
underwater acoustic target recognition (UATR) using ship-radiated noise.
Inspired by neural mechanism of auditory perception, this paper provides a new
deep neural network trained by original underwater acoustic signals with
depthwise separable convolution (DWS) and time-dilated convolution neural
network, named auditory perception inspired time-dilated convolution neural
network (ATCNN), and then implements detection and classification for
underwater acoustic signals. The proposed ATCNN model consists of learnable
features extractor and integration layer inspired by auditory perception, and
time-dilated convolution inspired by language model. This paper decomposes
original time-domain ship-radiated noise signals into different frequency
components with depthwise separable convolution filter, and then extracts
signal features based on auditory perception. The deep features are integrated
on integration layer. The time-dilated convolution is used for long-term
contextual modeling. As a result, like language model, intra-class and
inter-class information can be fully used for UATR. For UATR task, the
classification accuracy reaches 90.9%, which is the highest in contrast
experiment. Experimental results show that ATCNN has great potential to improve
the performance of UATR classification.
