# Sound field reconstruction in rooms: inpainting meets super-resolution

arXiv: https://arxiv.org/abs/2001.11263 · HTML: https://mlsanity.com/paper/2001.11263/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2020.

## Built on by (most cited first, 10 of 10)

- [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)
- [Generative adversarial networks with physical sound field priors](https://mlsanity.com/paper/2308.00426/) (2023)
- [Deep Sound Field Reconstruction in Real Rooms: Introducing the ISOBEL Sound Field Dataset](https://mlsanity.com/paper/2102.06455/) (2021)
- [A Physics-Informed Neural Network-Based Approach for the Spatial Upsampling of Spherical Microphone Arrays](https://mlsanity.com/paper/2407.18732/) (2024)
- [Solving Room Impulse Response Inverse Problems Using Flow Matching with Analytic Wiener Denoiser](https://mlsanity.com/paper/2602.00652/) (2026)
- [Room Transfer Function Reconstruction Using Complex-valued Neural Networks and Irregularly Distributed Microphones](https://mlsanity.com/paper/2402.04866/) (2024)
- [Gaussian Process Regression of Steering Vectors With Physics-Aware Deep Composite Kernels for Augmented Listening](https://mlsanity.com/paper/2509.02571/) (2025)
- [Neural personal sound zones with flexible bright zone control](https://mlsanity.com/paper/2512.10375/) (2025)
- [SF-Flow: Sound field magnitude estimation via flow matching guided by sparse measurements](https://mlsanity.com/paper/2605.10398/) (2026)

## Abstract (arXiv)

In this paper, a deep-learning-based method for sound field reconstruction is proposed. It is shown the possibility to reconstruct the magnitude of the sound pressure in the frequency band 30-300 Hz for an entire room by using a very low number of irregularly distributed microphones arbitrarily arranged. Moreover, the approach is agnostic to the location of the measurements in the Euclidean space. In particular, the presented approach uses a limited number of arbitrary discrete measurements of the magnitude of the sound field pressure in order to extrapolate this field to a higher-resolution grid of discrete points in space with a low computational complexity. The method is based on a U-net-like neural network with partial convolutions trained solely on simulated data, which itself is constructed from numerical simulations of Green's function across thousands of common rectangular rooms. Although extensible to three dimensions and different room shapes, the method focuses on reconstructing a two-dimensional plane of a rectangular room from measurements of the three-dimensional sound field. Experiments using simulated data together with an experimental validation in a real listening room are shown. The results suggest a performance which may exceed conventional reconstruction techniques for a low number of microphones and computational requirements.