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.

该论文提出了一种基于深度学习的方法，使用生成对抗网络（GANs）对声场进行时空重建。该方法利用平面波基础，并学习房间内压力的潜在统计分布，以从有限数量的测量中准确重建声场。该方法的性能经过两个已建立的数据集评估，并与最先进的方法进行比较。结果表明，该模型能够在准确性和能量保持方面实现改进的重建性能，尤其是在高频范围和超过测量区域的外推时。此外，所提出的方法能够处理不同数量和配置的测量位置而不影响性能。结果表明，该方法为使用生成模型进行声场重建提供了一种有前景的方法，允许对声学问题进行物理上明确的先验条件。