3 accepted papers
We propose a novel approach for semi-supervised extraction of a moving audio source of interest (SOI) applicable in reverberant and noisy environments. The blind part of the method is based on independent vector extraction (IVE) and uses the recently proposed constant separating vector (CSV) mixing…
We propose a novel algorithm for adaptive blind audio source extraction. The proposed method is based on independent vector analysis and utilizes the auxiliary function optimization to achieve high convergence speed. The algorithm is partially supervised by a pilot signal related to the source of in…
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.…