Greedy alternative for room geometry estimation from acoustic echoes: a subspace-based method

Abstract

In this paper, we present a greedy subspace method for the acoustic echoes labeling problem, which occurs in applications such as source localization and room geometry estimation. The orthogonal projection into the null space of the microphones position matrix is used to filter and sort all possible combinations of echoes. A greedy strategy, based on the rank constraint of Euclidean distance matrices (EDMs), is used on the sorted subset of echo combinations to extract the feasible combinations. Numerical simulations using room impulse responses (RIRs) from shoe-box shaped rooms show that the method provides improvements in terms of computational complexity and the number of required measurements with respect to a recently published graph-based method.