EXTRACTION OF INDEPENDENT VECTOR COMPONENT FROM UNDERDETERMINED MIXTURES THROUGH BLOCK-WISE DETERMINED MODELING
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Date
2019-05
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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.
Description
Subject(s)
Blind Source Extraction, Underdetermined Mixing, Independent Vector Analysis, Speech Enhancement