mlsanityData to 7 Oct 2026

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

arXiv 2407.18732 ↗First posted 26 Jul 2024eess.AS cs.LG cs.SDcited by 2

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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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 12 of its 39 references that are in the map. First posted on arXiv 26 Jul 2024.

Builds on

12 of 39 references.

References · 12

  1. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,214
  2. 2021Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functionscited by 49
  3. 2020Sound field reconstruction in rooms: inpainting meets super-resolutioncited by 10
  4. 2023Implicit neural representation with physics-informed neural networks for the reconstruction of the early part of room impulse responsescited by 8
  5. 2023Reconstruction of Sound Field through Diffusion Modelscited by 8
  6. 2024Physics-Informed Neural Network for Volumetric Sound field Reconstruction of Speech Signalscited by 6
  7. 2023Generative adversarial networks with physical sound field priorscited by 4
  8. 2023Sound Field Estimation around a Rigid Sphere with Physics-informed Neural Networkcited by 3
  9. 2023Acoustic source localization in the spherical harmonics domain exploiting low-rank approximationscited by 2
  10. 2023Circumvent spherical Bessel function nulls for open sphere microphone arrays with physics informed neural networkcited by 2
  11. 2024HOMULA-RIR: A Room Impulse Response Dataset for Teleconferencing and Spatial Audio Applications Acquired Through Higher-Order Microphones and Uniform Linear Microphone Arrayscited by 2
  12. 2024Room Transfer Function Reconstruction Using Complex-valued Neural Networks and Irregularly Distributed Microphonescited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026CNN Models for Microphone Array Covariance Matrix Upsampling and Acoustic Imagingcited by 0
  2. 2026Generative Learning for Ambisonic Upscalingcited by 0