A robust and passive method for geometric calibration of large arrays.
A robust and passive method for geometric calibration of large arrays.
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DOI：
10.1121/1.4944566
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发表时间：
2016-03
期刊：
影响因子：
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通讯作者：
C. Vanwynsberghe;P. Challande;J. Marchal;R. Marchiano;F. Ollivier
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文献类型：
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作者：
C. Vanwynsberghe;P. Challande;J. Marchal;R. Marchiano;F. Ollivier
本文提出了一个完整的策略，为任意形状的大型麦克风阵列的几何估计。这里的大意味着麦克风的数量（数百）和尺寸（几米）。这种阵列可以用于开放或密闭空间中的各种应用，如声学成像、源识别或语音处理。对于如此大的阵列系统，手工测量几何形状是不切实际的。为此，提出了一种盲被动检测方法。它是基于背景噪声的分析，假设是一个扩散场。拟议的战略分两步走。首先，成对麦克风的距离被确定通过匹配其测量的相干函数的扩散场理论预测的。其次，一个强大的多维缩放（MDS）算法的适应和实现。它利用局部特征来减少距离集并推断阵列的几何形状。这项工作是以前研究的延伸，它克服了未解决的缺点。特别是它有效地处理已知破坏标准MDS算法的离群值。这种能力的实验证明，通过治疗的情况下，两个阵列。他们表明，提出的改进管理大型空间阵列。
This paper presents a complete strategy for the geometry estimation of large microphone arrays of arbitrary shape. Largeness is intended here in both number of microphones (hundreds) and size (few meters). Such arrays can be used for various applications in open or confined spaces like acoustical imaging, source identification, or speech processing. For so large array systems, measuring the geometry by hand is impractical. Therefore a blind passive method is proposed. It is based on the analysis of the background acoustic noise, supposed to be a diffuse field. The proposed strategy is a two-step process. First the pairwise microphone distances are identified by matching their measured coherence function to the one predicted by the diffuse field theory. Second, a robust multidimensional scaling (MDS) algorithm is adapted and implemented. It takes advantage of local characteristics to reduce the set of distances and infer the geometry of the array. This work is an extension of previous studies, and it overcomes unsolved drawbacks. In particular it deals efficiently with the outliers known to ruin standard MDS algorithms. Experimental proofs of this ability are presented by treating the case of two arrays. They show that the proposed improvements manage large spatial arrays.