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首页> 外文期刊>IEEE transactions on audio, speech and language processing >Blind source separation based on a fast-convergence algorithm combining ICA and beamforming
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Blind source separation based on a fast-convergence algorithm combining ICA and beamforming

机译：基于ICA和波束赋形的快速收敛算法的盲源分离

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摘要

We propose a new algorithm for blind source separation (BSS), in which 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, (b) beamforming based on the estimated DOA, and (c) integration of (a) and (b) based on the algorithm diversity in both iteration and frequency domain. The unmixing 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. The results of the signal separation experiments reveal that the signal separation performance of the proposed algorithm is superior to that of the conventional ICA-based BSS method, even under reverberant conditions.
机译：我们提出了一种盲源分离（BSS）的新算法，该算法将独立分量分析（ICA）和波束成形相结合，通过在ICA中进行优化来解决慢收敛问题。所提出的方法包括以下三个部分：（a）具有到达方向（DOA）估计的频域ICA；（b）基于估计的DOA的波束成形；以及（c）（a）和（b）的积分）基于迭代和频域中的算法多样性。通过迭代优化，将基于ICA的解混矩阵在时间上替换为基于波束形成的矩阵，并且ICA与波束形成之间的时间交替可以实现快速和高收敛性优化。信号分离实验的结果表明，即使在混响条件下，该算法的信号分离性能也优于传统的基于ICA的BSS方法。

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