EXTRACTION OF INDEPENDENT VECTOR COMPONENT FROM UNDERDETERMINED MIXTURES THROUGH BLOCK-WISE DETERMINED MODELING

Abstract
We propose a new model for blind source extraction where the source of interest is assumed to be static while the background noise is dynamic. The model is determined within short blocks (the same number of sources as that of sensors), however, the noise subspace can be changing from block to block. We propose a gradient-based algorithm that jointly extracts an independent vector component from a set of mixtures obeying the model based on maximum quasi-likelihood principle. Simulations confirm the validity of the approach, and experiments with real-world recordings show promising results.
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Blind Source Extraction, Underdetermined Mixing, Independent Vector Analysis, Speech Enhancement
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