Joint Switching Dereverberation and Spatially Regularized Independent Vector Analysis

Switching independent vector analysis (SwIVA) is a blind source separation algorithm that employs a switching mechanism over multiple separation matrices to enhance separation accuracy. However, SwIVA suffers from the global permutation problem when prior information about the sources is unavailable. Furthermore, it exhibits poor performance in reverberant environments. In this paper, we extend SwIVA to a convolutional beamforming algorithm with a spatially regularized separation method (SR-SwCIVA). We conduct experiments to confirm that SR-SwCIVA achieves excellent separation performance under reverberant conditions while maintaining strong robustness against the global permutation problem.