Geometrically Constrained Room Modeling with Compact Microphone Arrays

flavio Ribeiro, Dinei Florencio, Demba Ba, and Cha Zhang

Abstract

The geometry of an acoustic environment can be an important information in many audio signal processing applications. To estimate such a geometry, previous work has relied on large microphone arrays, multiple test sources, moving sources or the assumption of a 2D room. In this paper, we lift these requirements and present a novel method that uses a compact microphone array to estimate a 3D room geometry, delivering effective estimates with low-cost hardware. Our approach first probes the environment with a known test signal emitted by a loudspeaker co-located with the array, from which the room impulse responses (RIRs) are estimated. It then uses an l1-regularized least-squares minimization to fit synthetically generated reflections to the RIRs, producing a sparse set of reflections. By enforcing structural constraints derived from the image model, these are classified into 1st, 2nd and 3rd-order reflections, thereby deriving the room geometry. Using this method, we detect walls using off-the-shelf teleconferencing hardware with a typical range resolution of about 1 cm. We present results using simulations and data from real environments.

Details

Publication typeArticle
Published inIEEE Transactions on Audio, Speech, and Language Processing
PublisherIEEE
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