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

TL;DR

This paper introduces new algorithms for independent component and vector extraction from nonstationary mixtures, leveraging nonstationary models and Gaussian assumptions to improve speed and accuracy in source separation tasks.

Contribution

It develops a novel extension of FastICA that handles nonstationary sources and mixtures, with a specific Gaussian model for frequency-domain speaker extraction.

Findings

Algorithms outperform state-of-the-art in convergence speed

Algorithms achieve higher extraction accuracy

Effective in frequency-domain speaker extraction

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.