Study on feature extraction method in border monitoring system using optimum wavelet packet decomposition
Border monitoring plays a key role in the national defense. In this study, we applied the sound identification technology on the border monitoring, and assumed an ideal border monitoring sound target recognition system. Feature extraction is a crucial step in this recognition system. As the border sounds are of non-stationary signals, the traditional methods failed to extract such kind of features. Fortunately, wavelet packet transform (WPT) can provide an arbitrary time-frequency decomposition for the signals. Based on WPT, a novel feature extraction method using optimum wavelet packet decomposition (OWPD) was proposed. According to the characteristics analysis of the border monitoring sounds using WPT, the signals were analyzed by selective multi-scale wavelet packet decomposition (i.e. OWPD), and then we built the meaningful and compact energy feature vectors as the input vectors of the BP neural network, in order to recognize the border monitoring sound. Extensive experimental results showed that this feature extraction method has convincing recognition efficiency.
feature extraction border monitoring recognition optimum wavelet packet decomposition (owpd) bp neural network