AnyRIR: Robust Non-intrusive Room Impulse Response Estimation in the Wild

TL;DR

AnyRIR introduces a robust, non-intrusive method for estimating room impulse responses in noisy, real-world environments using music as excitation, outperforming traditional techniques in challenging conditions.

Contribution

The paper presents a novel L1-norm regression approach for RIR estimation that effectively handles non-stationary noise without requiring dedicated test signals.

Findings

Outperforms L2-based deconvolution methods in noisy scenarios

Effective in environments with codec mismatch

Enables robust RIR estimation for AR/VR applications

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.