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Recently deep learning and machine learning approaches have been widelyemployed for various applications in acoustics. Nonetheless, in the area ofsound field processing and reconstruction classic methods based on thesolutions of wave equation are still widespread. Recently, physics-informedneural networks have been proposed as a deep learning paradigm for solvingpartial differential equations which govern physical phenomena, bridging thegap between purely data-driven and model based methods. Here, we exploitphysics-informed neural networks to reconstruct the early part of missing roomimpulse responses in an uniform linear array. This methodology allows us toexploit the underlying law of acoustics, i.e., the wave equation, forcing theneural network to generate physically meaningful solutions given only a limitednumber of data points. The results on real measurements show that the proposedmodel achieves accurate reconstruction and performance in line with respect tostate-of-the-art deep-learning and compress sensing techniques whilemaintaining a lightweight architecture.

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