RECURSIVE AND PARTIALLY SUPERVISED ALGORITHMS FOR SPEECH ENHANCEMENT ON THE BASIS OF INDEPENDENT VECTOR EXTRACTION
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Date
2018
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Publisher
IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Abstract
This paper introduces a recursive variant of the recently proposed independent vector extraction algorithm suitable for on-line blind source separation. Two partially supervised variants are proposed and tested. Both variants exploit known direction of arrival (DOA) of the source of interest (SOI). The first variant uses a pre-separated output of a DOA-steered MPDR beamformer as a pilot component to ensure the extraction of the SOI. In the second variant, a geometrical constraint is imposed to ensure that the separating vector does not stray too far from the assumed direction of the SOI. Experiments using simulated and real-world recordings are demonstrated. Both supervised variants show improvements compared to the unsupervised algorithm in terms of convergence stability and separation accuracy.
Description
Subject(s)
Blind Audio Source Separation, Speech Enhancement, Independent Component Analysis, Independent Vector Analysis, Independent Vector Extraction