Conveners
A17.05 Sound field estimation and reconstruction: S124
- Samuel A. Verburg (Technical University of Denmark)
- Elias Zea (Marcus Wallenberg Laboratory, KTH Royal Institute of Technology)
A17.05 Sound field estimation and reconstruction: P494
- Elias Zea (Marcus Wallenberg Laboratory, KTH Royal Institute of Technology)
- Samuel A. Verburg (Technical University of Denmark)
A17.05 Sound field estimation and reconstruction: S403
- Samuel A. Verburg (Technical University of Denmark)
- Elias Zea (Marcus Wallenberg Laboratory, KTH Royal Institute of Technology)
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Yueheng LI (Institute of Sound and Vibration Research)09/09/2026, 14:20A17 Signal Processing
Spatial aliasing in spherical microphone arrays (SMAs) arises from insufficient spatial sampling to capture high-order components, which are folded back into lower orders. This phenomenon is well understood: the frequency-independent spatial aliasing matrix characterises the mapping by which higher-order components are aliased into lower orders, as determined by the array’s sampling geometry....
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Augusto Fantinelli (Human-Environment Research group, La Salle - URL)09/09/2026, 14:40A17 Signal Processing
Parametric array loudspeakers use arrays of ultrasonic transducers to generate highly directive audible fields through the non-linear properties of air in the ultrasonic range. They require very high ultrasonic pressure levels to generate the audible field, but these levels, in turn, lead to measurement inaccuracies produced by the microphone. Known as spurious sound, this measurement error...
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Stefan Graham (Technical University of Denmark)09/09/2026, 15:00A17 Signal Processing
Acoustic scattering plays an important role in ultrasound, geophysics and object mapping for navigation. When the object geometry is unknown, reconstruction of the scattered acoustic field is challenging as the boundary conditions cannot be explicitly enforced. We incorporate a learnable Brinkman-style penalty into the governing wave equations. The penalty induces attenuation within the...
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Jesper Brunnström (Uppsala University)09/09/2026, 15:00A17 Signal Processing
Kernel ridge regression (KRR) has emerged as an effective approach to sound field estimation (SFE). By constructing the kernel and its associated reproducing kernel Hilbert space (RKHS) appropriately, the optimal KRR solution can be guaranteed to satisfy the wave equation. Such kernels and RKHSs have so far only been derived in the frequency domain and discrete-time domain. Therefore, in this...
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Matthias Blochberger (KU Leuven)09/09/2026, 15:20A17 Signal Processing
Plane-wave-based sound field estimation commonly relies on discretising the plane-wave domain into a finite set of propagation directions, which can lead to high-dimensional models and unfavourable conditioning in sparse inverse problems. In this work, we investigate continuous directional latent models by parameterising the plane-wave distribution on the unit sphere with compact mixtures. We...
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Elias Zea (Marcus Wallenberg Laboratory, KTH Royal Institute of Technology)09/09/2026, 16:00A17 Signal Processing
Reconstructing room-acoustic wavefields from sparse measurements remains challenging in the presence of diffraction and scattering, where multiple propagation components overlap in space–time. Classical representations, such as plane waves, provide global field expansions but require significant coefficients to capture localized interactions and destructive interference. Multi-scale...
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Elias Zea (Marcus Wallenberg Laboratory, KTH Royal Institute of Technology)09/09/2026, 16:20A17 Signal Processing
Parametric representations of wavefields are central to sound field estimation and reconstruction, where capturing both propagation geometry and localization is essential. We introduce a parametric framework, called Gaussian boostlet kernels, a variation of boostlets that uses Gaussian functions to control their frequency bandwidth and phase-speed selectivity (geometric spread). We explore the...
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Antonio Figueroa-Duran (Universidad Politécnica de Madrid)09/09/2026, 16:40A17 Signal Processing
Sound field reconstruction methods have increasingly been employed to estimate acoustic field quantities of complex sound fields across space. These approaches typically describe the observed field as a superposition of basis functions that satisfy the wave equation. Beyond established applications in sound source radiation, material characterisation, and active noise control, their use in...
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Samuel A. Verburg (Technical University of Denmark)09/09/2026, 17:00A17 Signal Processing
Sound field reconstruction from sparse microphone measurements is an ill-posed problem highly sensitive to modeling choices. Approaches based on elementary wave expansions, such as plane wave expansions (PWE) or the equivalent source method (ESM), are popular and widely used, but they require domain discretizations prone to model mismatch. More recent physics-informed neural networks (PINNs)...
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Louis Reine (LMSSC, CNAM Paris, HESAM Université)09/09/2026, 17:20A17 Signal Processing
Low-frequency sound reproduction in concert-halls can be significantly improved through the use of spatial sound field control methods. Such techniques may require accurate spatial sampling of the room transfer functions (RTFs) across the venue for every subwoofer loudspeaker. Respecting the Nyquist criteria spatially is impractical in real-world concert scenarios where measurement time is...
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Nara Hahn (Institute of Sound and Vibration Research)09/09/2026, 17:40A17 Signal Processing
This paper studies learning-based sound field control for a prototype large-audience scenario with ten loudspeakers and multiple listeners distributed over a listening region. Two identical complex-valued multilayer perceptrons are compared. One is trained on a boundary normal-velocity objective and one is trained directly on binaural ear pressures. The velocity-based network is first...
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