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

• URL: http://arxiv.org/abs/2407.18732v1
• Date: Fri, 26 Jul 2024 13:35:06 GMT
• Title: A Physics-Informed Neural Network-Based Approach for the Spatial Upsampling of Spherical Microphone Arrays
• Authors: Federico Miotello, Ferdinando Terminiello, Mirco Pezzoli, Alberto Bernardini, Fabio Antonacci, Augusto Sarti,
• Abstract summary: 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.
• Score: 40.98027720342511
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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