Deep PINNs for RIR Interpolation
Published:Dec 28, 2025 12:57
•1 min read
•ArXiv
Analysis
This paper addresses the problem of estimating Room Impulse Responses (RIRs) from sparse measurements, a crucial task in acoustics. It leverages Physics-Informed Neural Networks (PINNs), incorporating physical laws to improve accuracy. The key contribution is the exploration of deeper PINN architectures with residual connections and the comparison of activation functions, demonstrating improved performance, especially for reflection components. This work provides practical insights for designing more effective PINNs for acoustic inverse problems.
Key Takeaways
Reference
“The residual PINN with sinusoidal activations achieves the highest accuracy for both interpolation and extrapolation of RIRs.”