Room Impulse Response Completion Using Signal-Prediction Diffusion Models Conditioned on Simulated Early Reflections
Abstract
Room impulse responses (RIRs) are fundamental to audio data augmentation, acoustic signal processing, and immersive audio rendering. While geometric simulators such as the image source method (ISM) can efficiently generate early reflections, they lack the realism of measured RIRs due to missing acoustic wave effects. We propose a diffusion-based RIR completion method using signal-prediction conditioned on ISM-simulated direct-path and early reflections. Unlike state-of-the-art methods, our approach imposes no fixed duration constraint on the input early reflections. We further incorporate classifier-free guidance to steer generation toward a target distribution learned from physically realistic RIRs simulated with the Treble SDK. Objective evaluation demonstrates that the proposed method outperforms a state-of-the-art baseline in early RIR completion and energy decay curve reconstruction.
Keywords
Cite
@article{arxiv.2603.12442,
title = {Room Impulse Response Completion Using Signal-Prediction Diffusion Models Conditioned on Simulated Early Reflections},
author = {Zeyu Xu and Andreas Brendel and Albert G. Prinn and Emanuël A. P. Habets},
journal= {arXiv preprint arXiv:2603.12442},
year = {2026}
}
Comments
The following article has been submitted for review to Interspeech 2026