Fast Tracking of Time-Variant Systems Using Local Affine Subspaces

Abstract:
Various audio and speech processing applications require the identification and tracking of linear acoustic systems. Previous analyses have demonstrated that in many scenarios the set of possible impulse responses forms a low dimensional manifold. Existing approaches have used this fact to improve the convergence properties of an identification algorithm, e.g., by projecting the estimated impulse response vector onto a set of lower dimensional affine subspaces that are learned from data that is known a priori. In this paper, we present a novel variant of the Kalman filter that only tracks a low dimensional system representation in a linear subspace. Experimental results show that the proposed approach is robust in adverse signal-to-noise ratios and reduces the relative system distance compared to state-of-art approaches when tracking time-variant systems.