Border and Coastal Surveillance Sound Signal Recognition Based on the Optimum Wavelet Packet Basis

Wang yan; Li Zhi

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Border and Coastal Surveillance Sound Signal Recognition Based on the Optimum Wavelet Packet Basis

更新时间：2023-11-14

- Border and Coastal Surveillance Sound Signal Recognition Based on the Optimum Wavelet Packet Basis
- Advanced Engineering Sciences Vol. 43, Issue Z1, Pages: 151-154(2011)
- 作者机构：
  
  四川大学电子信息学院
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2011，
  
  Published Online：8 April 2011，
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王艳,李智.基于最佳小波包基的边海防声目标识别[J].工程科学与技术,2011,43(Z1):151-154.

Wang yan, Li Zhi. Border and Coastal Surveillance Sound Signal Recognition Based on the Optimum Wavelet Packet Basis. [J]. Advanced Engineering Sciences 43(Z1):151-154(2011)
王艳,李智.基于最佳小波包基的边海防声目标识别[J].工程科学与技术,2011,43(Z1):151-154. DOI：

Wang yan, Li Zhi. Border and Coastal Surveillance Sound Signal Recognition Based on the Optimum Wavelet Packet Basis. [J]. Advanced Engineering Sciences 43(Z1):151-154(2011) DOI：

摘要

中文摘要: 针对于声音识别技术应用于边海防目标的识别，提出了一种新的基于最佳小波包基能量的特征提取方法。具体方法是首先根据边海防声音信号的频率特征，对这些信号进行有选择地多尺度小波包分解，获取最佳小波包树，然后将相应的最佳小波包基的能量值归一化后作为特征向量，再将这些特征向量输入BP神经网络训练，用训练好的神经网络对边海防声音信号进行识别。仿真结果表明：在噪声条件下，利用此特征提取方法，识别率达到94%，比基于小波包分解的特征提取方法的识别率高出6个百分点。

Abstract

Abstract: Aimed at applying the sound recognition technology on the target recognition in border and coastal surveillance systems

a new method of feature extraction was proposed based on the optimum wavelet packet basis. According to the frequency characteristics of the collected border and coastal surveillance sound signals

these sound signals were analyzed by selective multi-scale wavelet packet decomposition and the optimum wavelet tree was obtained. From the high dimensional coefficients of the corresponding optimum wavelet packet basis

the meaningful and compact energy feature vectors were built. After that

these vectors were inputted in a BP neural network to train the network. The trained BP network was then used to detect border and coastal surveillance sound signals. The simulation results indicated that the identification ratio is up to 94% using this feature extraction method under noisy environment

improved 6% compared with the method based on wavelet packet decomposition.

关键词

边海防声音信号小波包最佳小波包分解BP神经网络

Keywords

border and coastal surveillancesound signalwavelet packetoptimum wavelet packet decompositionBP neural network

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