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

查看全文 作　　者：Nianbin [1]Wang;Ming [1,2]He;Jianguo [1]Sun;Hongbin [1]Wang;Lianke [1]Zhou;Ci [1]Chu;Lei [3]Chen 高影响力作者 机构地区：[1]College of Computer Science and Technology,Harbin Engineering University,Harbin,150001,China;[2]College of Computer and Information Engineering,Heilongjiang University of Science and Technology Harbin,150022,China;[3]College of Engineering and Computing,Georgia Southern University,Georgia,30458,USA高影响力机构 出　　处：《Computers, Materials & Continua》索引2019年第1期,共13页高影响力期刊 基　　金：This work was funded by the National Natural Science Foundation of China under Grant(Nos.61772152,61502037);the Basic Research Project(Nos.JCKY2016206B001,JCKY2014206C002,JCKY2017604C010)and the Technical Foundation Project(No.JSQB2017206C002). 摘　　要：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 wellsuited 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. 关 键 词：Noise PROCESSING UNDERWATER TARGET RECOGNITION convolutional NEURAL network
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