用深度残差网络提升声学脉冲响应插值精度
Spatial Interpolation of Room Impulse Responses based on Deeper Physics-Informed Neural Networks with Residual Connections
- 设计带残差连接的深层物理信息神经网络
- 正弦激活函数使插值与外推误差最低
- 适合声学反问题建模与高稳定深度网络设计
房间脉冲响应（RIR）在时不变线性假设下刻画了扬声器到麦克风的声音传播特性。从有限测量点估算RIR对声音传播分析与可视化至关重要。近年来，物理信息神经网络（PINNs）通过将基本物理规律嵌入深度学习模型，实现了高精度的RIR估计；然而，网络深度的作用尚未系统研究。本研究开发了一种具有残差连接的更深PINN架构，并分析了网络深度对估计性能的影响。进一步对比了tanh与正弦激活函数。结果表明，采用正弦激活函数的残差PINN在RIR插值与外推任务中均达到最高准确率。此外，该架构在增加深度时仍保持训练稳定性，并显著提升了反射分量的估计效果。这些结果为设计用于声学反问题的深度且稳定的PINN提供了实用指导。
原文摘要 · Abstract (English)
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.
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