Blind source separation based on a fast-convergence algorithm combining ICA and beamforming
Blind source separation based on a fast-convergence algorithm combining ICA and beamforming
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DOI：
10.1109/tsa.2005.855832
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发表时间：
2006-03-01
影响因子：
--
通讯作者：
Shikano, K
中科院分区：
文献类型：
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作者：
Saruwatari, H;Kawamura, T;Shikano, K
我们提出了一种用于盲源分离（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。所提出的方法由以下三部分组成：(a) 带有到达方向（DOA）估计的频域 ICA；(b) 基于到达方向估计的空波束成形；(c) 基于迭代和频域算法多样性的(a)和(b)集成。通过迭代优化，用基于空波束成形的矩阵对 ICA 得到的解混矩阵进行时空置换，ICA 和波束成形的时空交替可以实现快速、高收敛的优化。信号分离实验结果表明，即使在混响条件下，拟议算法的信号分离性能也优于传统的基于 ICA 的 BSS 方法。
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) null 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 null 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.