Cooperative Wiener-ICA for source localization and Separation by distributed microphone arrays
Cooperative Wiener-ICA for source localization and Separation by distributed microphone arrays
复制标题
协作 Wiener-ICA 通过分布式麦克风阵列进行源定位和分离
DOI：
10.1109/icassp.2010.5496294
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
M. Omologo
中科院分区：
文献类型：
--
作者：
F. Nesta;M. Omologo
在过去的十年中，为了提高扬声器定位系统在大型混响房间中的精度和空间覆盖范围，人们提出了分布式麦克风阵列。原则上，分布式传声器网络提供的框架也可以有效地应用于盲源分离（BSS）。分离通常是通过在单个适应步骤中处理在紧密间隔的麦克风上采样的信号来实现的，例如通过独立分量分析（ICA）。当传声器间距或源与传声器之间的距离增大时，由于空间混叠效应和传声器的空间相干性降低，分离性能下降。在本文中，我们提出了一种新的方法，这里称为合作维纳ICA (CW-ICA)，它能够将BSS应用于分布式麦克风阵列网络获取的信号。不同的ICA适应应用于每个阵列记录的信号，并相互连接，以约束每个适应收敛到与相同物理解释相关的解决方案。对两个阵列网络的初步分析表明，该方法可以成功地应用于源分离和定位任务。
During the last decade, distributed microphone arrays have been proposed in order to increase accuracy and spatial coverage of speaker localization systems operating in large and reverberant rooms. In principle, the framework provided by a distributed microphone network can also be applied effectively when using Blind Source Separation (BSS). Separation is commonly performed by processing the signals sampled at closely spaced microphones in a single adaptation step, for example by means of Independent Component Analysis (ICA). When the microphone spacing or the distance between source and microphones increase, the separation performance reduces due to spatial aliasing effects and to a reduced spatial coherence at microphones. In this paper we propose a new method, here referred to as Cooperative Wiener ICA (CW-ICA), which is able to apply BSS to signals acquired by a network of distributed microphone arrays. Different ICA adaptations are applied to the signals recorded by each array and are interconnected in order to constrain each adaptation to converge to a solution related to the same physical interpretation. A preliminary analysis on a network of two arrays shows that the proposed method can be applied successfully to source separation and localization tasks.