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.

翻译：本文提出了一种基于深度学习的方法，利用生成对抗网络（GANs）实现声场的时空重建。该方法采用平面波基，学习房间内声压的潜在统计分布，从而从有限数量的测量数据中精确重建声场。使用两个公开数据集对方法性能进行评估，并与当前最先进方法进行比较。结果表明，该模型在精度和能量保持方面实现了更优的重建性能，尤其是在高频区域及测量区域外推时表现突出。此外，该方法能够在不牺牲性能的前提下处理不同数量和配置的测量位置。研究结果表明，这一方法为使用生成模型进行声场重建提供了有前景的途径，该模型允许将物理先验信息引入声学问题。