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首页> 外文期刊>AEU: Archiv fur Elektronik und Ubertragungstechnik: Electronic and Communication >Study on feature extraction method in border monitoring system using optimum wavelet packet decomposition
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Study on feature extraction method in border monitoring system using optimum wavelet packet decomposition

机译：基于最优小波包分解的边界监测系统特征提取方法研究

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摘要

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.
机译：边境监测在国防中起着关键作用。在这项研究中，我们将声音识别技术应用于边界监视，并假设了理想的边界监视声音目标识别系统。特征提取是此识别系统中的关键步骤。由于边界声音是非平稳信号，因此传统方法无法提取此类特征。幸运的是，小波包变换（WPT）可以为信号提供任意的时频分解。提出了一种基于WPT的最优小波包分解（OWPD）特征提取方法。根据WPT对边界监测声音的特征分析，通过选择性多尺度小波包分解（即OWPD）分析信号，然后建立有意义且紧凑的能量特征向量作为BP神经网络的输入向量。 ，以便识别边界监视声音。大量的实验结果表明，该特征提取方法具有令人信服的识别效率。

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