Spatial Interpolation of Room Impulse Responses based on Deeper Physics-Informed Neural Networks with Residual Connections

TL;DR

This paper introduces a deeper physics-informed neural network with residual connections and sinusoidal activations to improve room impulse response estimation, demonstrating enhanced accuracy and stability in sound propagation modeling.

Contribution

The study systematically investigates the impact of network depth in PINNs for RIR estimation and proposes a residual architecture with sinusoidal activations for better performance.

Findings

Residual PINN with sinusoidal activations outperforms other configurations.

Deeper networks with residual connections enable stable training.

Significant improvements in estimating reflection components.

Abstract

The room impulse response (RIR) characterizes sound propagation in a room from a loudspeaker to a microphone under the linear time-invariant assumption. Estimating RIRs from a limited number of measurement points is crucial for sound propagation analysis and visualization. Physics-informed neural networks (PINNs) have recently been introduced for accurate RIR estimation by embedding governing physical laws into deep learning models; however, the role of network depth has not been systematically investigated. In this study, we developed a deeper PINN architecture with residual connections and analyzed how network depth affects estimation performance. We further compared activation functions, including tanh and sinusoidal activations. Our results indicate that the residual PINN with sinusoidal activations achieves the highest accuracy for both interpolation and extrapolation of RIRs. Moreover, the proposed architecture enables stable training as the depth increases and yields notable improvements in estimating reflection components. These results provide practical guidelines for designing deep and stable PINNs for acoustic-inverse problems.