Rank-constrained spatial covariance matrix estimation (RCSCME) is a state-of-the-art blind speech extraction method applied to cases where one directional target speech and diffuse noise are mixed. In this paper, we proposed a new algorithmic extension of RCSCME. RCSCME complements a deficient one rank of the diffuse noise spatial covariance matrix, which cannot be estimated via preprocessing such as independent low-rank matrix analysis, and estimates the source model parameters simultaneously. In the conventional RCSCME, a direction of the deficient basis is fixed in advance and only the scale is estimated; however, the candidate of this deficient basis is not unique in general. In the proposed RCSCME model, the deficient basis itself can be accurately estimated as a vector variable by solving a vector optimization problem. Also, we derive new update rules based on the EM algorithm. We confirm that the proposed method outperforms conventional methods under several noise conditions.

中文翻译：

---

盲语音提取中秩受限空间协方差矩阵估计方法的噪声空间协方差矩阵估计不足

秩受限的空间协方差矩阵估计（RCSCME）是一种最新的盲语音提取方法，适用于将一个定向目标语音和弥散噪声混合在一起的情况。在本文中，我们提出了RCSCME的新算法扩展。RCSCME补充了弥散噪声空间协方差矩阵的一个不足的秩，该秩不能通过诸如独立低秩矩阵分析之类的预处理来估计，并且同时估计源模型参数。在传统的RCSCME中，缺陷基础的方向是预先确定的，只估计规模。但是，这种缺乏基础的候选人通常并不是唯一的。在提出的RCSCME模型中，可以通过解决向量优化问题将不足的基础本身准确地估计为向量变量。还，我们基于EM算法得出新的更新规则。我们确认，在几种噪声条件下，提出的方法优于传统方法。