Double Nonstationarity: Blind Extraction of Independent Nonstationary Vector/Component from Nonstationary Mixtures -- Algorithms

Abstract

In this article, nonstationary mixing and source models are combined for developing new fast and accurate algorithms for Independent Component or Vector Extraction (ICE/IVE), one of which stands for a new extension of the well-known FastICA. This model allows for a moving source-of-interest (SOI) whose distribution on short intervals can be (non-)circular (non-)Gaussian. A particular Gaussian source model assuming tridiagonal covariance matrix structures is proposed. It is shown to be beneficial in the frequency-domain speaker extraction problem. The algorithms are verified in simulations. In comparison to the state-of-the-art algorithms, they show superior performance in terms of convergence speed and extraction accuracy.

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References in corpus (7)

• Gradient Algorithms for Complex Non-Gaussian Independent Component/Vector Extraction, Question of Convergence
• Independent Vector Analysis: Identification Conditions and Performance Bounds
• Dynamic Independent Component/Vector Analysis: Time-Variant Linear Mixtures Separable by Time-Invariant Beamformers
• Cramér-Rao Bounds for Complex-Valued Independent Component Extraction: Determined and Piecewise Determined Mixing Models
• Performance Analysis of Source Image Estimators in Blind Source Separation
• Target Speech Extraction: Independent Vector Extraction Guided by Supervised Speaker Identification
• Performance Analysis of the Gaussian Quasi-Maximum Likelihood Approach for Independent Vector Analysis