Supervised system identification based on local PCA models

Abstract

We propose a supervised system identification method for recovering an acoustic impulse response in a reverberant room. Unlike most existing methods, our algorithm is based on prior information given in the form of a training set of known impulse responses acquired in a controlled environment. By relying on the prior information, we train local Principal Component Analysis (PCA) models of impulse responses corresponding to several different regions in the room. We propose to crudely localize the respective source position, and subsequently, based on the appropriate local model, recover the impulse response. In order to approximate the source location, we introduce a specially-tailored distance measure which is based on an affinity between the trained local models. Experimental results in simulated noisy and reverberant environments demonstrate significant improvements over existing methods.