  Accurate and reliable identification of the RTF between microphones with
respect to a desired source is an essential component in the design of
microphone array beamformers, specifically the MVDR criterion. Since an
accurate estimation of the RTF in a noisy and reverberant environment is a
cumbersome task, we aim at leveraging prior knowledge of the acoustic enclosure
to robustify the RTF estimation by learning the RTF manifold. In this paper, we
present a novel robust RTF identification method, tested and trained with real
recordings, which relies on learning the RTF manifold using a GCN to infer a
robust representation of the RTF in a confined area, and consequently enhance
the beamformer's performance.
