Paper Title:
Border and Coastal Surveillance Sound Target Classification Based on Optimum Wavelet Packet Decomposition
  Abstract

The present work contributes to the field of border and coastal surveillance sound target classification. A new feature extraction method is proposed based on the optimum wavelet packet decomposition (OWPD). According to the frequency characteristic of border and coastal surveillance sound signals, each signal is decomposed by selective multi-scale wavelet packet decomposition (WPD) and the OWPD tree is obtained. From their high dimension OWPD coefficients, we build the meaningful and compact energy feature vectors, then use them as the input vectors of the BP neural network to classify the border and coastal surveillance sound types. Extensive experimental results show that the classification efficiency is up to 94% using this feature extraction method, improved 6% compared with the method based on WPD.

  Info
Periodical
Advanced Materials Research (Volumes 225-226)
Edited by
Helen Zhang, Gang Shen and David Jin
Pages
725-728
DOI
10.4028/www.scientific.net/AMR.225-226.725
Citation
Y. Wang, Z. Li, "Border and Coastal Surveillance Sound Target Classification Based on Optimum Wavelet Packet Decomposition", Advanced Materials Research, Vols. 225-226, pp. 725-728, 2011
Online since
April 2011
Authors
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Price
$32.00
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