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

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.