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Sparse plane wave decomposition of a low frequency sound field within a cylindrical cavity using spherical microphone arrays

机译:使用球形麦克风阵列圆柱形腔内低频声场的稀疏平面波分解

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Conventionally plane wave decomposition (PWD) of a low frequency sound field using a spherical microphone array (SMA) would suffer from low spatial resolution. Although compressive sensing (CS) has been employed to estimate a sparse set of plane waves when formulated in the spherical harmonics domain, its performance at the low frequency is still not fully discussed, particularly if the plane waves are densely discretized, the columns of the sensing matrix will become highly correlated. To address these problems, a two-step l(1)-norm minimization method for the PWD is developed. First, a sufficient set of sound field coefficients in the spherical harmonics domain is solved using CS, which is equivalent to the sparse spherical harmonics decomposition (SHD), however, with the sparsity constraint imposed on the plane-wave basis instead of the coefficients vector. With the estimated coefficients, a sparse set of plane waves can then be recovered using CS by requiring that the truncated order is sufficiently high. By means of a scan-based measurement with a feasible SMA, and with the sparsity constraint imposed on the plane-wave basis, the proposed method proved effective in improving spatial resolution with less measurements through both simulations and experiments within a cylindrical cavity. (C) 2018 Elsevier Ltd. All rights reserved.
机译:使用球形麦克风阵列(SMA)的低频声场的传统平面波分解(PWD)将遭受低空间分辨率。虽然已经采用压缩感测(CS)来估计在球面谐波域中配制时估计一组稀疏平面波,但其在低频处的性能仍然没有完全讨论,特别是如果平面波被密集地离散,则感测矩阵将变得高度相关。为了解决这些问题,开发了两步L(1)-norm最小化方法的PWD。首先,使用CS求解球面谐波域中的足够的声场系数,其等同于稀疏球面谐波分解(SHD),然而,在平面波基础上施加的稀疏限制而不是系数矢量。利用估计的系数,然后可以通过要求截短的顺序足够高,使用CS恢复稀疏的平面波组。通过基于扫描的测量,具有可行的SMA,并且具有在平面波的基础上施加的稀疏约束,所提出的方法证明通过模拟和圆柱形腔内的实验来改善空间分辨率。 (c)2018年elestvier有限公司保留所有权利。

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