@Article{cmc.2019.03709,
AUTHOR = {Nianbin  Wang, Ming  He, Jianguo  Sun, Hongbin  Wang, Lianke  Zhou, Ci  Chu, Lei  Chen},
TITLE = {ia-PNCC: Noise Processing Method for Underwater Target Recognition Convolutional Neural Network},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {58},
YEAR = {2019},
NUMBER = {1},
PAGES = {169--181},
URL = {http://www.techscience.com/cmc/v58n1/23000},
ISSN = {1546-2226},
ABSTRACT = {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.},
DOI = {10.32604/cmc.2019.03709}
}