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Empirical mode decomposition based near-field equivalence source imaging system for sound identification

机译:基于经验模式分解的近场等效声源成像系统

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The conventional microphone array near-field Fourier acoustic holography using Discrete Fourier Transform (DFT) is able to efficiently reconstruct sound field and acquire an image of noise distribution. However, Fourier transform causes measuring error in practical applications, and people have to select primary frequency for observing sound field holography based on the spectrum of source signal. In this paper, we use the empirical mode decomposition (EMD) owing to its completeness, orthogonality, and adaptiveness, which are able to decompose multiple sound sources in the spatial domain and acquire instantaneous frequencies via intrinsic mode functions (IMFs). Prior information about the primary frequency is not necessary by this approach that makes the simultaneous observation of each source possible. In addition, EMD sound source imaging approach may be integrated into a near-field equivalent source imaging (NESI) system, which includes a virtual microphone technology generally used for sound field image enhancement. We have implemented and compared the constituent 1D EMD, 2D EMD spatial transform systems, and EMD based NESI approach in Labview language. Several experimental results and detailed discussions are also provided to verify the characteristics of multiple sound sources.
机译:使用离散傅立叶变换(DFT)的常规麦克风阵列近场傅立叶声学全息术能够有效地重建声场并获取噪声分布的图像。然而,傅立叶变换在实际应用中会引起测量误差,人们不得不根据源信号的频谱来选择用于观察声场全息的主要频率。在本文中,由于经验模态分解(EMD)的完整性,正交性和自适应性,我们可以使用它们来分解空间域中的多个声源,并通过固有模态函数(IMF)获取瞬时频率。通过这种方法,可以同时观察每个信号源,而不必事先获得有关主频率的信息。另外,EMD声源成像方法可以被集成到近场等效源成像(NESI)系统中,该系统包括通常用于声场图像增强的虚拟麦克风技术。我们已经用Labview语言实现并比较了构成1D EMD,2D EMD空间变换系统和基于EMD的NESI方法。还提供了一些实验结果和详细讨论来验证多种声源的特性。

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