An Auditory Convolutional Neural Network for Underwater Acoustic Target Timbre Feature Extraction and Recognition

In order to extract the line-spectrum features of underwater acoustic targets in complex environments, an auditory convolutional neural network (ACNN) with the ability of frequency component perception, timbre perception and critical information perception is proposed in this paper inspired by the human auditory perception mechanism. This model first uses a gammatone filter bank that mimics the cochlear basilar membrane excitation response to decompose the input time-domain signal into a number of sub-bands, which guides the network to perceive the line-spectrum frequency information of the underwater acoustic target. A sequence of convolution layers is then used to filter out interfering noise and enhance the line-spectrum components of each sub-band by simulating the process of calculating the energy distribution features, after which the improved channel attention module is connected to select line spectra that are more critical for recognition, and in this module, a new global pooling method is proposed and applied in order to better extract the intrinsic properties. Finally, the sub-band information is fused using a combination layer and a single-channel convolution layer to generate a vector with the same dimensions as the input signal at the output layer. A decision module with a Softmax classifier is added behind the auditory neural network and used to recognize the five classes of vessel targets in the ShipsEar dataset, achieving a recognition accuracy of 99.8%, which is improved by 2.7% compared to the last proposed DRACNN method, and there are different degrees of improvement over the other eight compared methods. The visualization results show that the model can significantly suppress the interfering noise intensity and selectively enhance the radiated noise line-spectrum energy of underwater acoustic targets.