AnyRIR: Robust Non-intrusive Room Impulse Response Estimation in the Wild
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents AnyRIR, a novel method for robust RIR estimation using music as an excitation signal, significantly advancing the field of acoustic measurement in noisy environments. The combination of innovative methodology and thorough experimental validation positions this work as a meaningful contribution to audio signal processing and machine learning applications in acoustics.
Methodology
The proposed AnyRIR method innovatively utilizes music as an excitation signal for RIR estimation, which is a significant departure from traditional methods that rely on controlled signals. The formulation of the problem as an L1-norm regression in the time-frequency domain is well-justified, particularly for handling non-stationary noise. The use of IRLS and LSMR for efficient computation demonstrates a solid understanding of optimization techniques suitable for large-scale problems. However, the paper could benefit from a deeper exploration of the theoretical underpinnings of the L1-norm approach compared to other methods.
Results & evaluation
The experiments conducted on both simulated and real-world data are robust, showcasing the method's effectiveness in various scenarios, including codec mismatches and non-stationary noise. The comparison with baseline methods (L2-norm and frequency-domain deconvolution) is well-executed, providing clear evidence of AnyRIR's advantages. However, the paper could improve by including more diverse real-world environments to further validate the method's generalizability.
Reproducibility
The authors provide a clear implementation of their method, with links to a GitHub repository and a demo page. The detailed description of the algorithms and the preprocessing steps enhances reproducibility. However, the paper lacks specific details on the datasets used, which could hinder full reproducibility for external researchers.
Limitations
One limitation is the reliance on the assumption that non-stationary noise can be effectively modeled as outliers in the L1-norm framework. In highly dynamic environments with complex noise patterns, this assumption may not hold. Additionally, while the method shows promise in noisy environments, its performance in extremely noisy or chaotic settings remains to be tested.
Broader impact
The AnyRIR method has significant implications for applications in AR/VR, smart speakers, and other audio technologies, where accurate acoustic modeling is crucial. By enabling robust RIR estimation in uncontrolled environments, it opens avenues for more immersive audio experiences in public spaces. The paper presents AnyRIR, a novel method for robust RIR estimation using music as an excitation signal, significantly advancing the field of acoustic measurement in noisy environments. The combination of innovative methodology and thorough experimental validation positions this work as a meaningful contribution to audio signal processing and machine learning applications in acoustics.
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