Open Access

ARTICLE

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

Nianbin Wang¹, Ming He^1,2, Jianguo Sun^1,*, Hongbin Wang¹, Lianke Zhou¹, Ci Chu¹, Lei Chen³

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.

* Corresponding Author: Jianguo Sun. Email: .

Computers, Materials & Continua 2019, 58(1), 169-181. https://doi.org/10.32604/cmc.2019.03709

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.

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