The variable span linear filters (VSLFs) constitute a unified framework of conventional subspace and linear filtering techniques for noise reduction. The construction of VSLFs, however, relies on the generalized eigendecomposition (GEVD) methods, which are computationally expensive. This in turn stymies the employment of such filters in practical online processing problems. To address this issue, we first propose in this paper a fixed-point iteration technique to extract the generalized eigenvectors. It is based on maximizing the pre-whitened generalized Rayleigh quotient (GRQ). We then integrate this technique with online statistic estimation to construct VSLFs. Our proposed method is computationally efficient and can also harness parallel architectures. To show its effectiveness, we consider a speech enhancement application and compare the results with those of several existing methods.

中文翻译：

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使用定点方法在线构建可变跨度线性滤波器

可变跨度线性滤波器（VSLF）构成了常规子空间和线性滤波技术的统一框架，用于降低噪声。但是，VSLF的构建依赖于广义本征分解（GEVD）方法，该方法在计算上非常昂贵。这反过来阻碍了在实际的在线处理问题中使用此类过滤器。为了解决这个问题，我们首先提出一种定点迭代技术来提取广义特征向量。它基于最大化预加白的广义瑞利商（GRQ）。然后，我们将该技术与在线统计估计相集成，以构造VSLF。我们提出的方法计算效率高，并且还可以利用并行体系结构。为了展示其有效性，