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

• 作者: Ci Chu Hongbin Wang Jianguo Sun Lei Chen Lianke Zhou Ming He Nianbin Wang
• 来源期刊: 计算机、材料和连续体(英文)
• 学科: 物理学
• 关键词: Noise PROCESSING UNDERWATER TARGET RECOGNITION convolutional NEURAL network
• 年，卷（期）: 2019,（1）
• 页码范围: 169-181
• 页数: 13页
• 分类号: O42

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.