Supervised Independent Vector Analysis Through Pilot Dependent Components
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Date
2017-01-01
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Abstract
Unknown global permutation of the separated sources, timevarying source activity and under determination are common problems affecting on-line Independent Vector Analysis when applied to real-world speech enhancement. In this
work we propose to extend the signal model of IVA by
introducing additional supervising components. Pilot signals,
wh ich are dependent on the sources, are injected in the
multidimensional source representation and act as a prior
knowledge. The resulting adaptation still maximizes the multivariate source independence, while simultaneously forcing
the estimation of sources dependent on the pilot components.
It is also shown as the S-IVA is a generalization over the
previously proposed weighted Natural Gradient. Numerical
evaluations shows the effectiveness of the proposed method
in challenging real-world applications.
Description
Subject(s)
Independent vector analysis, source separation, independent component analysis, speech enhancement
Citation
ISSN
15206149
ISBN
978-1-5090-4117-6