首页> 外文期刊>EURASIP journal on audio, speech, and music processing >Time-Domain Convolutive Blind Source Separation Employing Selective-Tap Adaptive Algorithms
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Time-Domain Convolutive Blind Source Separation Employing Selective-Tap Adaptive Algorithms

机译:采用选择性抽头自适应算法的时域卷积盲源分离

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摘要

We investigate novel algorithms to improve the convergence and reduce the complexity of time-domain convolutive blind source separation (BSS) algorithms. First, we propose MMax partial update time-domain convolutive BSS (MMax BSS) algorithm. We demonstrate that the partial update scheme applied in the MMax LMS algorithm for single channel can be extended to multichannel time-domain convolutive BSS with little deterioration in performance and possible computational complexity saving. Next, we propose an exclusive maximum selective-tap time-domain convolutive BSS algorithm (XM BSS) that reduces the interchannel coherence of the tap-input vectors and improves the conditioning of the autocorrelation matrix resulting in improved convergence rate and reduced misalignment. Moreover, the computational complexity is reduced since only half of the tap inputs are selected for updating. Simulation results have shown a significant improvement in convergence rate compared to existing techniques.
机译:我们研究新颖的算法,以提高收敛性,并减少时域卷积盲源分离(BSS)算法的复杂性。首先,我们提出了MMax部分更新时域卷积BSS(MMax BSS)算法。我们证明了在MMax LMS算法中为单通道应用的部分更新方案可以扩展到多通道时域卷积BSS,而性能几乎没有下降,并且可能节省了计算复杂性。接下来,我们提出了一种专有的最大选择性抽头时域卷积BSS算法(XM BSS),该算法减少了抽头输入向量的通道间相干性,并改善了自相关矩阵的条件,从而提高了收敛速度并减少了失准。此外,由于仅选择了抽头输入的一半用于更新,因此降低了计算复杂度。仿真结果表明,与现有技术相比,收敛速度有了显着提高。

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