Generative adversarial networks with physical sound field priors

This repository contains the code for the paper Generative adversarial networks with physical sound field priors accepted for publication in The Journal of the Acoustical Society of America (2023).

Abstract

This paper presents a deep learning-based approach for the spatio-temporal reconstruction of sound fields using Generative Adversarial Networks (GANs). The method utilises a plane wave basis and learns the underlying statistical distributions of pressure in rooms to accurately reconstruct sound fields from a limited number of measurements. The performance of the method is evaluated using two established datasets and compared to state-of-the-art methods. The results show that the model is able to achieve an improved reconstruction performance in terms of accuracy and energy retention, particularly in the high-frequency range and when extrapolating beyond the measurement region. Furthermore, the proposed method can handle a varying number of measurement positions and configurations without sacrificing performance. The results suggest that this approach provides a promising approach to sound field reconstruction using generative models that allow for a physically informed prior to acoustics problems

Usage

To create a conda environment with all the required dependencies, execute the following command in your terminal:

conda env create -f environment.yml

An illustrative example of sound field inference can be found in the notebook ./notebooks/bandwidth_extension_example.ipynb. To run this notebook, please start by executing the scripts ./data/Inference files/dl_gen_weights.py and ./data/Inference files/dl_meshrir.py to download the necessary files.

Citation

If you use this work, please consider including the following citation in your research:

@article{karakonstantis2023generative,
  title={Generative Adversarial Networks with Physical Sound Field Priors},
  author={Karakonstantis, Xenofon and Fernandez-Grande, Efren},
  journal={The Journal of the Acoustical Society of America},
  volume={154},
  number={2},
  pages={1226--1238},
  year={2023},
  publisher={AIP Publishing}
}