  Most existing methods in binaural sound source localization rely on some kind
of aggregation of phase-and level-difference cues in the time-frequency plane.
While different ag-gregation schemes exist, they are often heuristic and suffer
in adverse noise conditions. In this paper, we introduce the rectified binaural
ratio as a new feature for sound source local-ization. We show that for
Gaussian-process point source signals corrupted by stationary Gaussian noise,
this ratio follows a complex t-distribution with explicit parameters. This new
formulation provides a principled and statistically sound way to aggregate
binaural features in the presence of noise. We subsequently derive two simple
and efficient methods for robust relative transfer function and time-delay
estimation. Experiments on heavily corrupted simulated and speech signals
demonstrate the robustness of the proposed scheme.
