Computationally-Efficient Overdetermined Blind Source Separation Based on Iterative Source Steering
Computationally-Efficient Overdetermined Blind Source Separation Based on Iterative Source Steering
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DOI：
10.1109/lsp.2021.3134939
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发表时间：
2022-01-01
影响因子：
3.9
通讯作者：
Kawahara, Tatsuya
中科院分区：
文献类型：
--
作者：
Du, Yicheng;Scheibler, Robin;Kawahara, Tatsuya
提出了一种计算效率高的过定混合盲源分离优化算法。在确定的情况下，已经提出了一种矩阵反求的迭代源导向（ISS）算法来估计平方分解矩阵，作为一种计算效率高的替代流行的迭代投影（IP）算法。IP算法基于源方向（即行方向）的除混矩阵更新，并自然地扩展到称为OverIVA的超确定独立向量分析（IVA）。相比之下，ISS算法在每次更新时都会改变整个分解矩阵，使其对超定情况的扩展变得不平凡。在本文中，我们针对OverIVA提出了一种改进的ISS算法，充分利用了ISS的计算节省。我们还利用改进的ISS算法推导了独立低秩矩阵分析（OverILRMA）的过定扩展。实验结果表明，本文提出的基于iss的OverIVA和OverILRMA在计算成本更低的情况下，在语音分离性能上与基于ip的传统语音分离方法相当或更好。
This paper describesa computationally-efficient optimization algorithm for the blind source separation (BSS) of overdetermined mixtures. In the determined case, a matrix-inversion-free iterative source steering (ISS) algorithm has been proposed for estimating a square demixing matrix as a computationally-efficient alternative to the popular iterative projection (IP) algorithm. The IP algorithm is based on source-wise (i.e., row-wise) updates of the demixing matrix, and lends itself naturally to an extension to overdetermined independent vector analysis (IVA) called OverIVA. In contrast, the ISS algorithm changes the whole demixing matrix at every update, making its extension to the overdetermined case non-trivial. In this paper, we propose a modified ISS algorithm for OverIVA fully exploiting the computational savings of ISS. We also derive an overdetermined extension of independent low-rank matrix analysis (OverILRMA) with the modified ISS algorithm. Experimental results showed that the proposed ISS-based OverIVA and OverILRMA were comparable or superior to the conventional IP-based counterparts in speech separation performance while achieving lower computational cost.