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Fast-convergence blind separation of more than two sources combining ICA and beamforming

机译：结合ICA和波束成形的两个以上源的快速收敛盲分离

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摘要

Summary form only given. We propose a new blind source separation (BSS) algorithm for multiple source signals. Independent component analysis (ICA) and beamforming are combined to resolve the slow-convergence problem through optimization in ICA. The proposed method consists of the following three parts: (a) frequency-domain ICA with direction-of-arrival (DOA) estimation using a Lloyd clustering algorithm; (b) beamforming based on the estimated DOA; (c) integration of (a) and (b) based on the algorithm diversity in both iteration and frequency domain. The separation matrix obtained by ICA is temporally substituted by the matrix based on beamforming through iterative optimization, and the temporal alternation between ICA and beamforming can realize fast- and high-convergence optimization. Experimental results reveal that the source-separation performance of the proposed algorithm is superior to that of the conventional ICA-based BSS method, even under reverberant conditions.
机译：摘要表格仅给出。我们为多个源信号提出了一种新的盲源分离（BSS）算法。独立分量分析（ICA）和波束成形组合以通过ICA的优化来解决缓慢收敛问题。该方法包括以下三个部分：（a）频域ICA，使用LLOYD聚类算法进行到达方向（DOA）估计; （b）基于估计的DOA的波束成形; （c）基于迭代和频率域中的算法多样性的（a）和（b）的集成。通过ICA获得的分离矩阵基于通过迭代优化基于波束成形的矩阵逐时地代替，并且ICA和波束成形之间的时间交替可以实现快速和高收敛优化。实验结果表明，即使在混响条件下，所提出的算法的源分离性能优于传统的基于ICA的BSS方法。

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