Abstract
Sound reproduction systems may highly benefit from detailed knowledge of the acoustic space to enhance the spatial sound experience. This article presents a room geometry inference method based on identification of reflective boundaries using a high-resolution direction-of-arrival map produced via room impulse responses (RIRs) measured with a linear loudspeaker array and a single microphone. Exploiting the sparse nature of the early part of the RIRs, Elastic Net regularization is applied to obtain a 2D polar-coordinate map, on which the direct path and early reflections appear as distinct peaks, described by their propagation distance and direction of arrival. Assuming a separable room geometry with four side-walls perpendicular to the floor and ceiling, and imposing pre-defined geometrical constraints on the walls, the 2D-map is segmented into six regions, each corresponding to a particular wall. The salient peaks within each region are selected as candidates for the first-order wall reflections, and a set of potential room geometries is formed by considering all possible combinations of the associated peaks. The room geometry is then inferred using a cost function evaluated on the higher-order reflections computed via beam tracing. The proposed method is tested with both simulated and measured data.
Highlights
I NFORMATION on the acoustic environment is of importance in advanced audio systems, improving the system performance and enabling new functionalities in applications
As the first attempt to combat this challenging situation, the main contributions of this work include: 1) a novel sparsity-constrained high-resolution 2D polarspace DOA mapping technique using room impulse responses (RIRs) recorded with a synchronized setup made up of a linear loudspeaker array and a single microphone, 2) a semisupervised approach tackling the geometrical ambiguity and identifying potential first-order reflection candidates through the segmentation of the DOA map into bounded regions, each associated with a wall, based on pre-defined constraints for wall dimensions and orientations, 3) room geometry inference based on a cost function measuring the match between the higher-order reflections estimated via beam tracing [27]–[29] and the actual reflections spotted on the DOA map
In the first step of the proposed Room geometry inference (RGI) algorithm, RIRs recorded synchronously between the linear loudspeaker array and single microphone are used to generate a DOA map, where the localization of the real- and imagemicrophones in 3D is achieved by their projection onto the 2D polar-coordinate space
Summary
I NFORMATION on the acoustic environment is of importance in advanced audio systems, improving the system performance and enabling new functionalities in applications. As the first attempt to combat this challenging situation, the main contributions of this work include: 1) a novel sparsity-constrained high-resolution 2D polarspace DOA mapping technique using RIRs recorded with a synchronized setup made up of a linear loudspeaker array and a single microphone, 2) a semisupervised approach tackling the geometrical ambiguity and identifying potential first-order reflection candidates through the segmentation of the DOA map into bounded regions, each associated with a wall, based on pre-defined constraints for wall dimensions and orientations, 3) room geometry inference based on a cost function measuring the match between the higher-order reflections estimated via beam tracing [27]–[29] and the actual reflections spotted on the DOA map.
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