Habets Emanuël A P, Cohen Israel, Gannot Sharon
School of Engineering, Bar-Ilan University, Ramat-Gan 52900, Israel.
J Acoust Soc Am. 2008 Nov;124(5):2911-7. doi: 10.1121/1.2987429.
Noise fields encountered in real-life scenarios can often be approximated as spherical or cylindrical noise fields. The characteristics of the noise field can be described by a spatial coherence function. For simulation purposes, researchers in the signal processing community often require sensor signals that exhibit a specific spatial coherence function. In addition, they often require a specific type of noise such as temporally correlated noise, babble speech that comprises a mixture of mutually independent speech fragments, or factory noise. Existing algorithms are unable to generate sensor signals such as babble speech and factory noise observed in an arbitrary noise field. In this paper an efficient algorithm is developed that generates multisensor signals under a predefined spatial coherence constraint. The benefit of the developed algorithm is twofold. Firstly, there are no restrictions on the spatial coherence function. Secondly, to generate M sensor signals the algorithm requires only M mutually independent noise signals. The performance evaluation shows that the developed algorithm is able to generate a more accurate spatial coherence between the generated sensor signals compared to the so-called image method that is frequently used in the signal processing community.
在现实生活场景中遇到的噪声场通常可以近似为球形或圆柱形噪声场。噪声场的特性可以用空间相干函数来描述。出于模拟目的，信号处理领域的研究人员通常需要呈现特定空间相干函数的传感器信号。此外，他们通常还需要特定类型的噪声，例如时间相关噪声、由相互独立的语音片段混合而成的嘈杂语音或工厂噪声。现有算法无法生成在任意噪声场中观察到的诸如嘈杂语音和工厂噪声之类的传感器信号。本文开发了一种高效算法，该算法可在预定义的空间相干约束下生成多传感器信号。所开发算法的优点有两方面。首先，对空间相干函数没有限制。其次，为了生成M个传感器信号，该算法仅需要M个相互独立的噪声信号。性能评估表明，与信号处理领域常用的所谓图像方法相比，所开发的算法能够在生成的传感器信号之间产生更精确的空间相干性。
