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Microphone array optimization by stochastic region contraction

机译:通过随机区域收缩优化麦克风阵列

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摘要

The authors deal with optimal microphone placement and gain for a linear one-dimensional array often in a confined environment. A power spectral dispersion function (PSD) is used as a core element for a min-max objective function (PSDX). Derivation of the optimal spacings and gains of the microphones is a hard computational problem since the min-max objective function exhibits multiple local minima (hundreds or thousands). The authors address the computational problem of finding the global optimal solution of the PSDX function. A new method, stochastic region contraction (SRC), is proposed. It achieves a computational speedup of 30-50 when compared to the commonly used simulated-annealing method. SRC is ideally suited for coarse-gain parallel processing.
机译:作者通常在狭窄的环境中处理线性一维阵列的最佳麦克风位置和增益。功率谱色散函数(PSD)用作最小最大目标函数(PSDX)的核心元素。麦克风的最佳间距和增益的推导是一个困难的计算问题,因为最小-最大目标函数表现出多个局部最小值(数百或数千)。作者解决了寻找PSDX函数的全局最优解的计算问题。提出了一种新的随机区域收缩方法。与常用的模拟退火方法相比,它可实现30-50的计算速度。 SRC非常适合粗增益并行处理。

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