On Joint Dereverberation and Source Separation with Geometrical Constraints and Iterative Source Steering

In order to improve both the separation performanceand the convergence speed, several geometrically constrainedindependent vector analysis (GC-IVA) algorithms have beendeveloped. Those algorithms are based on the multiplicativetransfer function model, which assumes that the analysis windowlength is longer than the effective part of the room impulseresponses. However, this assumption does often not hold inreverberant environments, particularly if the reverberation isstrong, which makes the algorithms suffer from significantperformance degradation. To circumvent this issue, an algorithmwas developed, which jointly optimizes the weighted predictionerror (WPE) dereverberation method and GC-IVA (GC-WPE-IVA). While it has demonstrated promising performance, thisjoint optimization method involves matrix inversion; so it iscomputationally very expensive. This work attempts to improvethe efficiency and stability of GC-WPE-IVA. We develop aniterative source steering (ISS) updating algorithm in the frame-work of GC-WPE-IVA. The experimental results show that thedeveloped method is computationally much more efficient yet itcan achieve comparable separation performance in reverberationenvironments as compared to GC-WPE-IVA.