2 citations · 3 across the 4 of their papers we have counts for
4 papers · 1 filter
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…
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…