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

TL;DR

This paper introduces a physics-informed neural network method with Rowdy activations to enhance the spatial resolution of spherical microphone arrays, enabling high-quality upsampling from limited capsules and outperforming existing signal processing techniques.

Contribution

The paper presents a novel neural network approach that incorporates physical constraints for effective spatial upsampling of spherical microphone arrays with fewer capsules.

Findings

Outperforms state-of-the-art signal processing methods in upsampling quality

Leverages physical constraints for high-order signal reconstruction

Effective with limited number of microphone capsules

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.