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Conference Paper

Compact statistical encoding of early reflections for auditory virtual reality

Authors: Raghuvanshi, Nikunj; Allen, Andrew; Snyder, John; Chemistruck, Michael; Cross, Noel; Walker, Christopher; Willette, Aaron; McWilliams, Nathan; Myrbeck, Shane

AES Conference: AES 2022 International Audio for Virtual and Augmented Reality Conference · Paper 36 · August 2022

Abstract

Early reflections pose a major challenge for parametric virtual acoustics systems. Reflections within a spatial impulse response (IR) must be compactly encoded to allow interactive rendering within limited resources. Encoding individual echoes is far too expensive, while the exponentially-decaying-noise model only befits late reverberation. We propose a novel statistical formulation for compactly encoding salient early reflection properties with six interpretable, orthogonal parameters capturing the loudness, echo density, temporal mean & spread, and directional mean & spread of energy arrival within the IR. Further, we propose a robust approach for additive separation of the impulse response into a coherent (specular) and incoherent (diffuse) component, rather than partitioning at a mixing time. We discuss initial results on a variety of measured IRs in indoor and outdoor spaces suggesting that salient acoustical variations across scenes are captured by the proposed parameters.

Details

Published in
AES Conference: AES 2022 International Audio for Virtual and Augmented Reality Conference
Paper number
36
Publication date
August 6, 2022
Session subject
Paper
Affiliation
Microsoft, Redmond, WA, USA; Arup, Los Angeles, CA, USA (See document for exact affiliation information.)
Type
Conference Paper