TY  - EJOU
AU  - Wang, Nianbin  
AU  - He, Ming  
AU  - Sun, Jianguo  
AU  - Wang, Hongbin  
AU  - Zhou, Lianke  
AU  - Chu, Ci  
AU  - Chen, Lei  

TI  - ia-PNCC: Noise Processing Method for Underwater Target Recognition Convolutional Neural Network
T2  - Computers, Materials \& Continua

PY  - 2019
VL  - 58
IS  - 1
SN  - 1546-2226

AB  - Underwater target recognition is a key technology for underwater acoustic countermeasure. 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 to underwater target recognition. Improved anti-noise Power-Normalized Cepstral Coefficients (ia-PNCC) is proposed, based on PNCC applied to underwater noises. Multitaper and normalized Gammatone filter banks are applied to improve the anti-noise capacity. The method is combined with a convolutional neural network in order to recognize the underwater target. Experiment results show that the acoustic feature presented by ia-PNCC has lower noise and are well-suited to underwater target recognition using a 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 Cepstral Coefficients), the combination of the ia-PNCC with a convolutional neural network offers better accuracy for underwater target recognition.
KW  - Noise processing
KW  -  underwater target recognition
KW  -  convolutional neural network

DO  - 10.32604/cmc.2019.03709