A Physics-Informed Neural Network-Based Approach for the Spatial Upsampling of Spherical Microphone Arrays
<|reference_start|>A Physics-Informed Neural Network-Based Approach for the Spatial Upsampling of Spherical Microphone Arrays: 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.<|reference_end|>
@article{miotello2024a,
    title={A Physics-Informed Neural Network-Based Approach for the Spatial
  Upsampling of Spherical Microphone Arrays},
    author={Federico Miotello, Ferdinando Terminiello, Mirco Pezzoli, Alberto
  Bernardini, Fabio Antonacci, Augusto Sarti},
    journal={arXiv preprint arXiv:2407.18732},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.18732},
    primaryClass={eess.AS cs.LG cs.SD eess.SP}
}