Rank-constrained spatial covariance matrix estimation (RCSCME) is a blind speech extraction method utilized under the condition that one-directional target speech and diffuse background noise are mixed. In this paper, we propose a new model extension of RCSCME. RCSCME simultaneously conducts both the deficient rank-1 component complementation of the diffuse noise spatial covariance matrix, which is incompletely estimated by preprocessing methods such as independent low-rank matrix analysis, and the estimation of the source model parameters. In the conventional RCSCME, between the two parameters constituting the deficient rank-1 component, only the scale is estimated, whereas the other parameter, the deficient basis, is fixed in advance; however, how to choose the fixed deficient basis is not unique. In the proposed RCSCME model, we also regard the deficient basis as a parameter to estimate. As the generative model of an observed signal, we utilized the super-Gaussian generalized Gaussian distribution, which achieves better separation performance than the Gaussian distribution in the conventional RCSCME. Assuming the model, we derive new majorization-minimization (MM)- and majorization-equalization (ME)-algorithm-based update rules for the deficient basis. In particular, among innumerable ME-algorithm-based update rules, we successfully find an ME-algorithm-based update rule with a mathematical proof supporting the fact that the step of the update rule is larger than that of the MM-algorithm-based update rule. We confirm that the proposed method outperforms conventional methods under several simulated noise conditions and a real noise condition.

秩约束空间协方差矩阵估计（RCSCME）是一种在单向目标语音和扩散背景噪声混合的条件下使用的盲语音提取方法。在本文中，我们提出了 RCSCME 的新模型扩展。RCSCME同时进行扩散噪声空间协方差矩阵的缺乏秩1分量补足，该矩阵通过独立低秩矩阵分析等预处理方法不完全估计，以及源模型参数的估计。在传统的 RCSCME 中，在构成缺陷 rank-1 分量的两个参数之间，只估计了尺度，而另一个参数，即缺陷基，是预先固定的；然而，如何选择固定缺陷基并不是唯一的。在提出的 RCSCME 模型中，我们也将有缺陷的基础作为估计的参数。作为观测信号的生成模型，我们利用了超高斯广义高斯分布，它比传统RCSCME中的高斯分布具有更好的分离性能。假设该模型，我们为缺陷基推导出新的基于专业化最小化 (MM) 和专业化均衡 (ME) 算法的更新规则。特别是，在无数基于 ME 算法的更新规则中，我们成功地找到了一个基于 ME 算法的更新规则，其数学证明支持更新规则的步长大于基于 MM 算法的更新的事实。规则。我们证实，在几种模拟噪声条件和真实噪声条件下，所提出的方法优于传统方法。