发表机构
The Australian National University; The University of Queensland（澳大利亚国立大学; 昆士兰大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对基于ISM的RIR模拟计算成本高的问题，提出物理引导框架PathRIR，保留ISM几何结构并学习保留重要路径，用轻量级补偿多层感知器恢复能量，实验表明其能减少计算、提高效率并降低误差。
AI 中文摘要
基于图像源法（ISM）的房间脉冲响应（RIR）模拟是声学场景建模中一种有用且具有物理可解释性的工具，但随着反射阶数和房间复杂度增加，全阶ISM计算成本高昂。我们提出一种用于快速RIR模拟的物理引导框架，在在线遍历过程中保留ISM几何结构，同时学习仅保留声学上重要的图像源路径。为恢复因修剪而去除的能量，所提出的PathRIR使用轻量级补偿多层感知器预测缺失的尾端能量包络并生成能量遵循该包络的补偿尾端。在不规则三维房间上的实验表明，PathRIR减少了图像源计算，提高了运行时效率，同时实现了低波形和衰减相关误差。消融结果表明，添加补偿尾端提高了波形保真度，降低了能量衰减曲线误差、混响时间误差和直达混响比误差，且运行时开销适中。
英文摘要
Image-source-method (ISM)-based room impulse response (RIR) simulation is a useful and physically interpretable tool for acoustic scene modeling, but full-order ISM becomes computationally expensive as the reflection order and room complexity increase. We propose a physics-guided framework for fast RIR simulation that preserves the geometric structure of ISM while learning to retain only acoustically important image-source paths during online traversal. To recover energy removed by pruning, the proposed PathRIR uses a lightweight compensation multilayer perceptron to predict the missing late-tail energy envelope and generate a compensation tail whose energy follows that envelope. Experiments on irregular 3D rooms show that PathRIR reduces image-source computation and improves runtime efficiency over a full-order ISM simulator, while achieving low waveform- and decay-related errors. Ablation results show that adding the compensation tail improves waveform fidelity and reduces energy-decay-curve error, reverberation-time error, and direct-to-reverberant-ratio error, with modest runtime overhead.
CommentsPublished in IWAENC 2026. Code and data: https://github.com/ShaoHenry/PathRIR
