A Physics-Informed Neural Network-Based Approach for the Spatial Upsampling of Spherical Microphone Arrays
Abstract
Spherical microphone arrays are convenient tools for capturing the spatial characteristics of a sound field. However, achieving superior spatial resolution requires arrays with numerous capsules, consequently leading to expensive devices. To address this issue, we present a method for spatially upsampling spherical microphone arrays with a limited number of capsules. Our approach exploits a physics-informed neural network with Rowdy activation functions, leveraging physical constraints to provide high-order microphone array signals, starting from low-order devices. Results show that, within its domain of application, our approach outperforms a state of the art method based on signal processing for spherical microphone arrays upsampling.
Keywords
Cite
@article{arxiv.2407.18732,
title = {A Physics-Informed Neural Network-Based Approach for the Spatial Upsampling of Spherical Microphone Arrays},
author = {Federico Miotello and Ferdinando Terminiello and Mirco Pezzoli and Alberto Bernardini and Fabio Antonacci and Augusto Sarti},
journal= {arXiv preprint arXiv:2407.18732},
year = {2024}
}
Comments
Accepted for publication at IWAENC 2024