  This paper introduces a new framework for supervised sound source
localization referred to as virtually-supervised learning. An acoustic shoe-box
room simulator is used to generate a large number of binaural single-source
audio scenes. These scenes are used to build a dataset of spatial binaural
features annotated with acoustic properties such as the 3D source position and
the walls' absorption coefficients. A probabilistic high- to low-dimensional
regression framework is used to learn a mapping from these features to the
acoustic properties. Results indicate that this mapping successfully estimates
the azimuth and elevation of new sources, but also their range and even the
walls' absorption coefficients solely based on binaural signals. Results also
reveal that incorporating random-diffusion effects in the data significantly
improves the estimation of all parameters.
