11 citations · 14 across the 10 of their papers we have counts for
10 papers
Masked Wavelet Scattering Transform Neural Field for Sound Field Reconstruction
Xinmeng Luan, Samuel A. Verburg, Efren Fernandez-Grande +1
In this paper, we propose a reconstruction framework that leverages the Wavelet Scattering Transform (WST) as a multi-scale feature extractor to impose statistical priors under spa…
Differentiable physics for sound field reconstruction
Samuel A. Verburg, Efren Fernandez-Grande, Peter Gerstoft
Sound field reconstruction involves estimating sound fields from a limited number of spatially distributed observations. This work introduces a differentiable physics approach for…
Morphogenesis of sound creates acoustic rainbows
Rasmus E. Christiansen, Ole Sigmund, Efren Fernandez-Grande
Sound is an essential sensing element for many organisms in nature, and multiple species have evolved organic structures that create complex acoustic scattering and dispersion phen…
Physics-Informed Neural Network for Volumetric Sound field Reconstruction of Speech Signals
Marco Olivieri, Xenofon Karakonstantis, Mirco Pezzoli +3
Recent developments in acoustic signal processing have seen the integration of deep learning methodologies, alongside the continued prominence of classical wave expansion-based app…
In situ sound absorption estimation with the discrete complex image source method
Eric Brandao, William Fonseca, Paulo Mareze +4
Estimating the sound absorption in situ relies on accurately describing the measured sound field. Evidence suggests that modeling the reflection of impinging spherical waves is imp…
Efficient Sound Field Reconstruction with Conditional Invertible Neural Networks
Xenofon Karakonstantis, Efren Fernandez-Grande, Peter Gerstoft
In this study, we introduce a method for estimating sound fields in reverberant environments using a conditional invertible neural network (CINN). Sound field reconstruction can be…
Room impulse response reconstruction with physics-informed deep learning
Xenofon Karakonstantis, Diego Caviedes-Nozal, Antoine Richard +1
A method is presented for estimating and reconstructing the sound field within a room using physics-informed neural networks. By incorporating a limited set of experimental room im…
Generative adversarial networks with physical sound field priors
Xenofon Karakonstantis, Efren Fernandez-Grande
This paper presents a deep learning-based approach for the spatio-temporal reconstruction of sound fields using Generative Adversarial Networks (GANs). The method utilises a plane…
A convolutional plane wave model for sound field reconstruction
Manuel Hahmann, Efren Fernandez-Grande
Spatial sound field interpolation relies on suitable models to both conform to available measurements and predict the sound field in the domain of interest. A suitable model can be…
Semi-supervised source localization in reverberant environments with deep generative modeling
Michael J. Bianco, Sharon Gannot, Efren Fernandez-Grande +1
We propose a semi-supervised approach to acoustic source localization in reverberant environments based on deep generative modeling. Localization in reverberant environments remain…