Joint estimation of sound source location and boundary impedance with physics-driven cosparse regularization

Abstract

Indoor acoustic source localization can be efficiently performed by modeling the sound propagation in the room, and by solving the arising inverse problem by means of cosparse regularization and convex optimization techniques. However, previous methods relying on this approach used to assume the knowledge of a number of room characteristics: its geometry, the walls' absorption or reflexion properties, as well as the speed of sound. In this paper, we show that this model, and the corresponding algorithms, can be extended to the case where the specific acoustic impedance of the boundary is unknown. The proposed method allows to jointly estimate the boundary impedances and the sound pressure in the room, without any preliminary calibration phase, from the only knowledge of the room geometry. Validated on simulation, this new algorithm constitutes a important step towards practical applicability of sound field cosparse modeling.

FDTD Methods for 3-D Room Acoustics Simulation With High-Order Accuracy in Space and Time

“…Wave-based methods such as time-domain finite difference (FDTD) methods [1]- [3] are becoming essential tools for room acoustics simulation, and have applications to artificial reverberation [4], sound source localization [5]- [7] and boundary impedance estimation [7], [8], as well as potential for use in virtual reality applications [9]. As opposed to conventional geometric methods like image source and ray-tracing methods (see, e.g., [10]), wave-based methods can capture all aspects of sound propagation in rooms, including full wave diffraction.…”

Versatile and Scalable Cosparse Methods for Physics-Driven Inverse Problems

“…Example: the standard Leapfrog Method (LFM). As an example, we describe here the standard Leapfrog Method (LFM) applied to the discretization of a 2D, isotropic acoustic wave equation (6). Here, the domain Θ is 3 dimensional, with variables r x , r y (two spatial coordinates) and t (time).…”

Time Domain Velocity Vector for Retracing the Multipath Propagation

“…Approximating g 1 (f ) by a frequency-independent constant g 1 (as, e.g. in [22]), and by applying the inverse Fourier transform F −1 (·) to the series above, we get the TDVV v(t) for the one-reflection setting:…”