Implicit neural representation with physics-informed neural networks for the reconstruction of the early part of room impulse responses
This work addresses a domain-specific problem in acoustics for sound field processing, offering an incremental improvement by integrating physics constraints into neural networks.
The paper tackles the problem of reconstructing missing early parts of room impulse responses in an acoustic array by using physics-informed neural networks, achieving accurate results comparable to state-of-the-art deep learning and compressed sensing methods with a lightweight architecture.
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.