RECURSIVE AND PARTIALLY SUPERVISED ALGORITHMS FOR SPEECH ENHANCEMENT ON THE BASIS OF INDEPENDENT VECTOR EXTRACTION

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