Independent Vector Extraction Constrained on Manifold of Half-Length Filters
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2023-04-04
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"This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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Abstract
Independent Vector Analysis (IVA) is a popular
extension of Independent Component Analysis (ICA) for joint
separation of a set of instantaneous linear mixtures, with a
direct application in frequency-domain speaker separation or
extraction. The mixtures are parameterized by mixing matrices,
one matrix per mixture. This means that the IVA mixing model
does not account for any relationships between parameters across
the mixtures/frequencies. The separation proceeds jointly only
through the source model, where statistical dependencies of
sources across the mixtures are taken into account. In this paper,
we propose a mixing model for joint blind source extraction
where the mixing model parameters are linked across the
frequencies. This is achieved by constraining the set of feasible
parameters to the manifold of half-length separating filters, which
has a clear interpretation and application in frequency-domain
speaker extraction.