Causal Spatio-Temporal Sound Field Reconstruction
David Sundstr\"om, Filip Tronarp, Johan Lindstr\"om, Andreas Jakobsson

TL;DR
This paper introduces a causal spatio-temporal sound field reconstruction method using a finite-window linear estimator that accounts for correlations in measurements, improving real-time accuracy.
Contribution
It formulates a novel causal finite-window estimator based on a physically interpretable covariance model and proposes a sample selection strategy to optimize reconstruction quality.
Findings
Improved short-window sound field reconstruction over frequency domain methods.
The covariance model relates to classical diffuse-field coherence.
Sample selection reduces computational complexity and enhances accuracy.
Abstract
In sound field control applications, it is commonly assumed that one has access to an accurate representation of the sound field in the region of interest. This is a problematic assumption since the reconstruction of a sound field from available microphone measurements is especially challenging in real-time applications where only causal measurements are available. Notably, causal time-windowed observations introduce correlation between frequency components, making sound field reconstruction methods that process each frequency band independently sub-optimal. In this work, we formulate a causal finite-window spatio-temporal linear minimum mean-square error estimator for sound field reconstruction. The sound field is modeled as the solution to the wave equation driven by a stationary stochastic spatio-temporal source distribution, which induces a physically interpretable covariance…
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