# Physics-Informed Neural Network for Volumetric Sound field Reconstruction of Speech Signals

arXiv: https://arxiv.org/abs/2403.09524 · HTML: https://mlsanity.com/paper/2403.09524/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2024.

## Built on by (most cited first, 6 of 6)

- [A Physics-Informed Neural Network-Based Approach for the Spatial Upsampling of Spherical Microphone Arrays](https://mlsanity.com/paper/2407.18732/) (2024)
- [Point Neuron Learning: A New Physics-Informed Neural Network Architecture](https://mlsanity.com/paper/2408.16969/) (2024)
- [Hankel-FNO: Fast Underwater Acoustic Charting Via Physics-Encoded Fourier Neural Operator](https://mlsanity.com/paper/2512.06417/) (2025)
- [A Survey on World Models Grounded in Acoustic Physical Information](https://mlsanity.com/paper/2506.13833/) (2025)
- [SF-Flow: Sound field magnitude estimation via flow matching guided by sparse measurements](https://mlsanity.com/paper/2605.10398/) (2026)
- [PhysWave: Physics-Guided Latent Diffusion Models for Controllable Spatial Audio Generation](https://mlsanity.com/paper/2608.29549/) (2026)

## Abstract (arXiv)

Recent developments in acoustic signal processing have seen the integration of deep learning methodologies, alongside the continued prominence of classical wave expansion-based approaches, particularly in sound field reconstruction. Physics-Informed Neural Networks (PINNs) have emerged as a novel framework, bridging the gap between data-driven and model-based techniques for addressing physical phenomena governed by partial differential equations. This paper introduces a PINN-based approach for the recovery of arbitrary volumetric acoustic fields. The network incorporates the wave equation to impose a regularization on signal reconstruction in the time domain. This methodology enables the network to learn the underlying physics of sound propagation and allows for the complete characterization of the sound field based on a limited set of observations. The proposed method's efficacy is validated through experiments involving speech signals in a real-world environment, considering varying numbers of available measurements. Moreover, a comparative analysis is undertaken against state-of-the-art frequency-domain and time-domain reconstruction methods from existing literature, highlighting the increased accuracy across the various measurement configurations.