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

Abstract

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.

Accepted for publication at Forum Acusticum 2023

References in corpus (4)

• Implicit Neural Representations with Periodic Activation Functions
• Acoustic source localization in the spherical harmonics domain exploiting low-rank approximations
• Physics-informed convolutional neural network with bicubic spline interpolation for sound field estimation
• Kernel interpolation of acoustic transfer functions with adaptive kernel for directed and residual reverberations

Cited by in corpus (1)

• Point Neuron Learning: A New Physics-Informed Neural Network Architecture