ANYRIR: ROBUST NON-INTRUSIVE ROOM IMPULSE RESPONSE ESTIMATION IN THE WILD
Kyung Yun Lee, Karolina Prawda, Vesa Välimäki, Sebastian J. Schlecht
Abstract
We address the problem of estimating room impulse responses (RIRs) in noisy, uncontrolled environments where non-stationary sounds such as speech or footsteps corrupt conventional deconvolution. We propose AnyRIR, a non-intrusive method that uses music as the excitation signal instead of a dedicated test signal, and formulate RIR estimation as an L1-norm regression in the time-frequency domain. Solved efficiently with Iterative Reweighted Least Squares (IRLS) and Least-Squares Minimal Residual (LSMR) methods, this approach exploits the sparsity of non-stationary noise to suppress its influence. Experiments on simulated and measured data show that AnyRIR outperforms L2-based and frequency-domain deconvolution, under in-the-wild noisy scenarios and codec mismatch, enabling robust RIR estimation for AR/VR and related applications.
BibTeX
@inproceedings{icassp2026_anyrirrobustnoni,
title = {ANYRIR: ROBUST NON-INTRUSIVE ROOM IMPULSE RESPONSE ESTIMATION IN THE WILD},
author = {Kyung Yun Lee and Karolina Prawda and Vesa Välimäki and Sebastian J. Schlecht},
booktitle = {ICASSP 2026},
year = {2026}
}