# Implicit neural representation with physics-informed neural networks for the reconstruction of the early part of room impulse responses

arXiv: https://arxiv.org/abs/2306.11509 · HTML: https://mlsanity.com/paper/2306.11509/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 46 references that are in the map.
- First posted on arXiv 20 Jun 2023.

## Builds on (most cited first, 9 of 9)

- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next](https://mlsanity.com/paper/2201.05624/) (2022)
- [Machine learning in acoustics: theory and applications](https://mlsanity.com/paper/1905.04418/) (2019)
- [Sound field reconstruction in rooms: inpainting meets super-resolution](https://mlsanity.com/paper/2001.11263/) (2020)
- [Kernel interpolation of acoustic transfer functions with adaptive kernel for directed and residual reverberations](https://mlsanity.com/paper/2303.03869/) (2023)
- [Physics-informed convolutional neural network with bicubic spline interpolation for sound field estimation](https://mlsanity.com/paper/2207.10937/) (2022)
- [Acoustic source localization in the spherical harmonics domain exploiting low-rank approximations](https://mlsanity.com/paper/2303.08480/) (2023)
- [Near field Acoustic Holography on arbitrary shapes using Convolutional Neural Network](https://mlsanity.com/paper/2103.16935/) (2021)

## Built on by (most cited first, 8 of 8)

- [Reconstruction of Sound Field through Diffusion Models](https://mlsanity.com/paper/2312.08821/) (2023)
- [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)
- [Room Transfer Function Reconstruction Using Complex-valued Neural Networks and Irregularly Distributed Microphones](https://mlsanity.com/paper/2402.04866/) (2024)
- [QFGN: A Quantum Approach to High-Fidelity Implicit Neural Representations](https://mlsanity.com/paper/2504.19053/) (2025)
- [Direction-Aware Neural Acoustic Fields for Few-Shot Interpolation of Ambisonic Impulse Responses](https://mlsanity.com/paper/2505.13617/) (2025)
- [Gaussian Process Regression of Steering Vectors With Physics-Aware Deep Composite Kernels for Augmented Listening](https://mlsanity.com/paper/2509.02571/) (2025)
- [Learning the Stellar Structure Equations via Self-supervised Physics-Informed Neural Networks](https://mlsanity.com/paper/2604.06255/) (2026)

## Abstract (arXiv)

Recently deep learning and machine learning approaches have been widely employed for various applications in acoustics. Nonetheless, in the area of sound field processing and reconstruction classic methods based on the solutions of wave equation are still widespread. Recently, physics-informed neural networks have been proposed as a deep learning paradigm for solving partial differential equations which govern physical phenomena, bridging the gap between purely data-driven and model based methods. Here, we exploit physics-informed neural networks to reconstruct the early part of missing room impulse responses in an uniform linear array. This methodology allows us to exploit the underlying law of acoustics, i.e., the wave equation, forcing the neural network to generate physically meaningful solutions given only a limited number of data points. The results on real measurements show that the proposed model achieves accurate reconstruction and performance in line with respect to state-of-the-art deep-learning and compress sensing techniques while maintaining a lightweight architecture.