Multi-Channel Source Separation Preserving Spatial Information

In this paper we propose two novel methods for preserving the spatial information in source separation algorithms. Our approach is applicable to any source separation algorithm and is based on an additional supervised adaptive filtering with the reference signals generated by the source separation system. If a special constrained optimization scheme is applied to derive the source separation algorithm then the novel approach can be simplified. The quality of the spatial representation and the separation performance of both methods and two state-of-the-art approaches from the literature have been evaluated by a MUSHRA listening test according to the relevant ITU recommendation showing that the novel methods clearly outperform the state-of-the-art approaches.