A Deep Convolutional Neural Network Inspired by Auditory Perception for Underwater Acoustic Target Recognition

Abstract

Underwater acoustic target recognition (UATR) using ship-radiated noise faces big challenges due to the complex marine environment. In this paper, inspired by neural mechanisms of auditory perception, a new end-to-end deep neural network named auditory perception inspired Deep Convolutional Neural Network (ADCNN) is proposed for UATR. In the ADCNN model, inspired by the frequency component perception neural mechanism, a bank of multi-scale deep convolution filters are designed to decompose raw time domain signal into signals with different frequency components. Inspired by the plasticity neural mechanism, the parameters of the deep convolution filters are initialized randomly, and the is n learned and optimized for UATR. The n, max-pooling layers and fully connected layers extract features from each decomposed signal. Finally, in fusion layers, features from each decomposed signal are merged and deep feature representations are extracted to classify underwater acoustic targets. The ADCNN model simulates the deep acoustic information processing structure of the auditory system. Experimental results show that the proposed model can decompose, model and classify ship-radiated noise signals efficiently. It achieves a classification accuracy of 81.96%, which is the highest in the contrast experiments. The experimental results show that auditory perception inspired deep learning method has encouraging potential to improve the classification performance of UATR.

Keywords: auditory perception inspired; brain-inspired; deep learning; filter learning; ship-radiated noise; underwater acoustic target recognition.

Figure 1

The architecture of ADCNN.

Figure 2

Spectrogram of recordings. (a) Cargo recording; (b) Passenger ship recording; (c) Tanker recording; (d) Environment noise recording.

Figure 3

Visualization of the output of each filter. (a) Testing sample of Cargo class; (b) Testing sample of Passenger ship class.

Figure 4

Result of t-SNE feature visualization. (a–e) Feature groups of deep filter sub-networks; (f) Features of layer-1; (g) Features of layer-2.

Figure 5

ROC curves of the proposed model and its competitors. (a) Cargo class is positive class; (b) Passenger ship class is positive class; (c) Tanker class is positive class; (d) Environment noise class is positive class.