Geometrically Constrained Independent Vector Analysis for Directional Speech Enhancement
Li Li, Kazuhito Koishida
Abstract
This paper addresses the multichannel directional speech enhancement problem with geometrically constrained independent vector analysis (GCIVA), where we aim to combine the high separation performance from blind source separation and the capability of directional focus from beamforming. The proposed method exploits geometric constraints composed from the spatial information of sources to guide the target speech to the desired output channel. A convergence-guaranteed parameter estimation algorithm is derived from the framework of auxiliary function-based IVA (AuxIVA) to take advantage of fast convergence, low computational cost, and no step-size tuning. We propose a dual-microphone speech enhancement system based on the proposed method and investigate its effectiveness with objective metrics. The experimental evaluations revealed that the proposed system outperformed the conventional beamforming and the standard AuxIVA in a large margin in terms of source-to-distortion and source-to-interference ratios.