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This research introduces PathRIR, a physics-guided framework that accelerates room impulse response simulation by using machine learning to prune non-critical acoustic paths while compensating for late-tail energy loss. By integrating a lightweight multilayer perceptron with the Image-Source Method, the approach significantly reduces computational costs without sacrificing waveform fidelity or reverberation accuracy in complex 3D environments. The work represents a notable advancement at the intersection of physical acoustics and machine learning optimization techniques.
PathRIR: Physics-Guided Acoustic Path Selection and Late-Tail Compensation for Fast Room Impulse Response Simulation
arXiv:2607.23293v1 Announce Type: cross Abstract: 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.
Abstract: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.
Accepted for publication in the Proceedings of the 19th International Workshop on Acoustic Signal Enhancement (IWAENC 2026). Project page: this https URL
Audio and Speech Processing (eess.AS); Machine Learning (cs.LG); Sound (cs.SD)
arXiv:2607.23293 [eess.AS]
(or arXiv:2607.23293v1 [eess.AS] for this version)
https://doi.org/10.48550/arXiv.2607.23293 arXiv-issued DOI via DataCite (pending registration)
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