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.

翻译：近年来，深度学习和机器学习方法已广泛应用于声学领域的各种任务中。尽管如此，在声场处理与重建领域，基于波动方程解的经典方法仍占主导地位。近期，物理信息神经网络被提出作为一种深度学习范式，用于求解支配物理现象的偏微分方程，从而弥合了纯数据驱动方法与基于模型方法之间的差距。本文利用物理信息神经网络来重建均匀线性阵列中缺失的房间脉冲响应的早期部分。该方法能够利用声学的基本规律（即波动方程），迫使神经网络在仅有有限数据点的情况下生成具有物理意义的解。真实测量结果表明，所提出的模型在保持轻量级架构的同时，实现了精确的重建，且性能与当前最先进的深度学习和压缩感知技术相当。