Geometrically Constrained TRINICON-based relative transfer function estimation in underdetermined scenarios

Abstract

Speech extraction in a reverberant enclosure using a linearly-constrained minimum variance (LCMV) beamformer usually requires reliable estimates of the relative transfer functions (RTFs) of the desired source to all microphones. In this contribution, a geometrically constrained (GC)-TRINICON concept for RTF estimation is proposed. This approach is applicable in challenging multiple-speaker scenarios and in underdetermined situations, where the number of simultaneously active sources outnumbers the number of available microphone signals. As a most practically relevant and distinctive feature, this concept does not require any voice-activity-based control mechanism. It only requires coarse reference information on the target direction of arrival (DoA). The proposed GC-TRINICON method is compared to a recently proposed subspace method for RTF estimation relying on voice-activity control. Experimental results confirm the effectiveness of GC-TRINICON in realistic conditions.