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Time Delay Estimation Method Based on Canonical Correlation Analysis

机译:基于典范相关分析的时延估计方法

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摘要

The localization of sources has numerous applications. To find the position of sources, the relative delay between two or more received signals for the direct signal must be determined. The generalized cross-correlation method is the most popular technique; however, an approach based on eigenvalue decomposition (EVD) is another popular one that utilizes the eigenvector of the minimum eigenvalue. The performance of the eigenvalue decomposition (EVD) based method degrades in low SNR and reverberation, because it is difficult to select a single eigenvector for the minimum eigenvalue. In this paper, we propose a new adaptive algorithm based on Canonical Correlation Analysis (CCA) to extend the operation SNR to the lower SNR and reverberation. The proposed algorithm uses an eigenvector that corresponds to the maximum eigenvalue in the generalized eigenvalue equation (GEVD). The estimated eigenvector contains all required information for time delay estimation. We have performed simulations with uncorrelated, correlated noise and reverberation for several SNRs, to show that time delays can be more accurately estimated (especially for low SNR) a CCA based algorithm versus the adaptive EVD algorithm.
机译:源的本地化有许多应用。为了找到源的位置,必须确定两个或更多个直接信号之间的相对接收信号延迟。广义互相关方法是最流行的技术。但是,基于特征值分解(EVD)的方法是另一种使用最小特征值的特征向量的流行方法。基于特征值分解(EVD)的方法的性能会降低SNR和混响,因为很难为最小特征值选择单个特征向量。在本文中,我们提出了一种基于规范相关分析(CCA)的新的自适应算法,以将操作SNR扩展到较低的SNR和混响。所提出的算法使用对应于广义特征值方程(GEVD)中最大特征值的特征向量。估计的特征向量包含所有用于延迟估计的信息。我们对几种SNR进行了不相关,相关噪声和混响的仿真,以表明与基于自适应EVD算法相比,基于CCA的算法可以更准确地估计时延(特别是对于低SNR)。

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