Relative transfer function identification on manifolds for supervised GSC beamformers

Identification of a relative transfer function (RTF) between two microphones is an important component of multichannel hands-free communication systems in reverberant and noisy environments. In this paper, we present an RTF identification method on manifolds for supervised generalized sidelobe canceler beamformers. We propose to learn the manifold of typical RTFs in a specific room using a novel extendable kernel method, which relies on common manifold learning approaches. Then, we exploit the extendable learned model and propose a supervised identification method that relies on both the a priori learned geometric structure and the measured signals. Experimental results show significant improvements over a competing method that relies merely on the measurements, especially in noisy conditions.