ia-PNCC: Noise Processing Method for Underwater Target Recognition Convolutional Neural Network

Underwater target recognition is a key technology for underwater acoustic countermeasure. And how to classify and recognize underwater targets according to the noise information of underwater targets has been a hot topic in the field of underwater acoustic signals. In this paper, the deep learning model is applied in the underwater target recognition and the improved anti-noise Power-Normalized Cepstral Coefficients (ia-PNCC) is proposed based on PNCC oriented to underwater target noise features. In this coefficient, multitaper and normalized Gammatone filter group are used to improve the anti-noise capacity of PNCC in underwater target recognition, and it is combined with the convolutional neural network to recognize the underwater target. The experiment results show that the acoustic feature presented by ia-PNCC is of higher anti-noise capacity and more adaptive to the underwater target recognition model of convolutional neural network. Compared with the combination of convolutional neural network with single acoustic feature such as MFCC (Mel-scale frequency cepstral coefficients) or LPCC (Linear Prediction PLP) and so on, the combination of ia-PNCC with convolutional neural network improves the underwater target recognition accuracy greatly.