Supervised source localization using diffusion kernels

Ronen Talmon, Israel Cohen, Sharon Gannot

Abstract: Recently, we introduced a method to recover the controlling parameters of linear systems using diffusion kernels. In this paper, we apply our approach to the problem of source localization in a reverberant room using measurements from a single microphone. Prior recordings of signals from various known locations in the room are required for training and calibration. The proposed algorithm relies on a computation of a diffusion kernel with a specially-tailored distance measure. Experimental results in a real reverberant environment demonstrate accurate recovery of the source location.