A Physics-Informed Neural Network-Based Approach for the Spatial Upsampling of Spherical Microphone Arrays

Federico Miotello, Ferdinando Terminiello, Mirco Pezzoli, Alberto Bernardini, Fabio Antonacci +1 more
7/26/2024
eess.AScs.LGcs.SD

No official code found. Best implementation: Ferdinando222/Upsampling-HOM (3 stars, MIT, last commit 2025-05-22).

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.

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Code Implementations(1)

Ferdinando222/Upsampling-HOMCommunity74%
MIT

The official repository for the paper: "A Physics-Informed Neural Network-Based Approach for the Spatial Upsampling of Spherical Microphone Arrays

30PythonSep 16, 20231 years agoMIT

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Cite this paper

@article{miotello2024physicsinformed,
  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},
  year   = {2024},
  eprint = {2407.18732},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2407.18732v1}
}

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