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

arXiv: https://arxiv.org/abs/2407.18732 · HTML: https://mlsanity.com/paper/2407.18732/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 39 references that are in the map.
- First posted on arXiv 26 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions](https://mlsanity.com/paper/2105.09513/) (2021)
- [Sound field reconstruction in rooms: inpainting meets super-resolution](https://mlsanity.com/paper/2001.11263/) (2020)
- [Implicit neural representation with physics-informed neural networks for the reconstruction of the early part of room impulse responses](https://mlsanity.com/paper/2306.11509/) (2023)
- [Reconstruction of Sound Field through Diffusion Models](https://mlsanity.com/paper/2312.08821/) (2023)
- [Physics-Informed Neural Network for Volumetric Sound field Reconstruction of Speech Signals](https://mlsanity.com/paper/2403.09524/) (2024)
- [Generative adversarial networks with physical sound field priors](https://mlsanity.com/paper/2308.00426/) (2023)
- [Sound Field Estimation around a Rigid Sphere with Physics-informed Neural Network](https://mlsanity.com/paper/2307.14013/) (2023)
- [Acoustic source localization in the spherical harmonics domain exploiting low-rank approximations](https://mlsanity.com/paper/2303.08480/) (2023)
- [Circumvent spherical Bessel function nulls for open sphere microphone arrays with physics informed neural network](https://mlsanity.com/paper/2308.00242/) (2023)
- [HOMULA-RIR: A Room Impulse Response Dataset for Teleconferencing and Spatial Audio Applications Acquired Through Higher-Order Microphones and Uniform Linear Microphone Arrays](https://mlsanity.com/paper/2402.13896/) (2024)
- [Room Transfer Function Reconstruction Using Complex-valued Neural Networks and Irregularly Distributed Microphones](https://mlsanity.com/paper/2402.04866/) (2024)

## Built on by (most cited first, 2 of 2)

- [CNN Models for Microphone Array Covariance Matrix Upsampling and Acoustic Imaging](https://mlsanity.com/paper/2607.01295/) (2026)
- [Generative Learning for Ambisonic Upscaling](https://mlsanity.com/paper/2609.23479/) (2026)

## Abstract (arXiv)

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.