Three-dimensional reflector localisation and room geometry estimation using a spherical microphone array

Abstract

The analysis of room impulse responses to localise reflecting surfaces and estimate room geometry is applicable in numerous aspects of acoustics, including source localisation, acoustic simulation, spatial audio, audio forensics, and room acoustic treatment. Geometry inference is an acoustic analysis problem where information about reflections extracted from impulse responses are used to localise reflective boundaries present in an environment, and thus estimate the geometry of the room. This problem, however, becomes more complex when considering non-convex rooms, as room shape cannot be constrained to a subset of possible convex polygons. This paper presents a geometry inference method for localising reflective boundaries and inferring the room's geometry for convex and non-convex room shapes. The method is tested using simulated room impulse responses for six scenarios, and real-world room impulse responses measured in a cuboid-shaped room, using a spherical microphone array containing multiple spatially distributed channels capable of capturing both time- and direction-of-arrival. Results show that the general shape of the rooms is inferred for each case, with a higher degree of accuracy for convex shaped rooms. However, inaccuracies generally arise as a result of the complexity of the room being inferred, or inaccurate estimation of time- and direction-of-arrival of reflections.

Acoustic source localization through the data fusion with multi-distributed microphone arrays

“…[19] developed a rescue robot by combining the speech recognition technology with an asymmetric pyramid microphone array. Spherical microphone array is widely used in acoustic and noise control systems [20] , room geometry estimation [21] , 3D audio equipment [22] and other fields due to its good symmetrical structure. However, the sound source localization based on a single microphone array has the disadvantages of narrow localization range, short localization distance and low localization accuracy.…”

Prediction of Object Geometry from Acoustic Scattering Using Convolutional Neural Networks

“…The parametric lines forming the walls are determined from common tangential lines of the ellipses. In another popular approach, the virtual image sources mirrored from real sources are used to determine the orientations of the walls [6,7,[10][11][12][13]15,17,20]. In this approach, to determine the wall orientation, a series of circles centered at different receiver locations are constructed.…”

“…Acoustic reflection models have been extensively researched to predict room geometry [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. A widely-used approach is based on the assumption that walls can be described by parametric lines in a 2-D plane [1, 3-5, 9, 14, 16, 19].…”

Power Response and Modal Decay Estimation of Room Reflections from Spherical Microphone Array Measurements Using Eigenbeam Spatial Correlation Model

“…However, these methods require a large number of RIR measurements for an accurate analysis of the room acoustic field. This problem was overcome with the introduction of higher-order spherical harmonic (eigenbeam)-based processing of spherical microphone array measurements [12,[26][27][28], which provided higher spatial resolution for analysis compared to the previous methods. Subsequently, more robust techniques [29][30][31] were developed to achieve efficient parameterization of the spatial sound field using modal decomposition.…”