Supervised Independent Vector Analysis Through Pilot Dependent Components

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