Applications of Spatially Localized Active-Intensity Vectors for Sound-Field visualization

Abstract

The purpose of this article is to detail and evaluate three alternative approaches to soundfield visualization, which all employ the use of spatially localized active-intensity (SLAI) vectors. These SLAI vectors are of particular interest, as they allow direction-of-arrival (DoA) estimates to be extracted in multiple spatially localized sectors, such that a sound source present in one sector has reduced influence on the DoA estimate made in another sector. These DoA estimates may be used to visualize the sound-field by either: (I) directly depicting the estimates as icons, with their relative size dictated by the corresponding energy of each sector; (II) generating traditional activity maps via histogram analysis of the DoA estimates; or (III) by using the DoA estimates to reassign energy and subsequently sharpen traditional beamformer-based activity maps. Since the SLAI-based DoA estimates are continuous, these approaches are inherently computationally efficient, as they forego the need for dense scanning grids to attain high-resolution imaging. Simulation results also show that these SLAI-based alternatives outperform traditional active-intensity and beamformer-based approaches, for the majority of cases.

Higher-Order Spatial Impulse Response Rendering: Investigating the Perceived Effects of Spherical Order, Dedicated Diffuse Rendering, and Frequency Resolution

“…By dividing the sound-field into sectors, problematic cases, such as multiple reflections arriving simultaneously from different directions, are analyzed more reliably [39] (assuming that these individual reflections do indeed fall within their own sector). Therefore, panning the spatially localized pressure components based on local DoA estimates, rather than panning the whole sound-field pressure based on a global DoA estimate, represents a more robust approach to rendering the direct stream when compared to first-order SIRR.…”

“…The new higher-order analysis conducted by the HO-SIRR method is based on first partitioning the sound-field into a number of uniformly distributed spatially localized sectors, S, which are then used to obtain spatially localized active-intensity vectors [40,39]; examples of sector patterns are depicted in Fig. 3.…”

“…where W S ∈ R 4S×(N +1) 2 is a beamforming matrix which generates the spatially localized components for all sectors, s ∈ R 4S×1 = [z (1) ; z (2) ; ...; z (S) ], where z (s) = [z (s) 00 , z (s) 1(−1) , z (s) 10 , z (s) 11 ] are the components for each individual sector. For details regarding the formulation of W S , the reader is referred to [6,40,39].…”

Parametric spatial post-filtering utilising high-order circular harmonics with applications to underwater sound-field visualisation

“…The primary downside of subspace methods, however, is that they require source detection algorithms (Akaike, 1974;Chen et al, 1991;Han and Nehorai, 2013) and can also be sensitive to correlated signals due to multipath propagation. Other grid-less DoA estimators include those derived from acoustic active-intensity (Fahy and Salmon, 1990;McCormack et al, 2019;Moore et al, 2017) and maximum likelihood estimators (Bianchi et al, 2015;Tervo and Politis, 2015).…”

A dataset of higher-order Ambisonic room impulse responses and 3D models measured in a room with varying furniture

“…They are the basis for research on spatial audio capture and reproduction [7], [8]. Moreover, they have also been bene-ficially used in recent sound-field analysis and visualization approaches [9], [10].…”