用音乐做声源，实现嘈杂环境下的鲁棒混响估计
AnyRIR: Robust Non-intrusive Room Impulse Response Estimation in the Wild
- 以音乐为激励信号，时间-频率域上做稀疏约束的回归
- 在真实噪声和编解码器不匹配下，性能优于传统方法
- 适合AR/VR等需实时混响建模的应用场景
针对噪声干扰、非平稳声音（如语音或脚步）污染的开放环境中的房间冲激响应（RIR）估计问题，本文提出AnyRIR——一种非侵入式方法。该方法利用音乐作为激励信号替代专用测试信号，并在时频域将RIR估计建模为L1范数回归问题。通过迭代重加权最小二乘法（IRLS）与最小残差最小二乘法（LSMR）高效求解，充分利用非平稳噪声的稀疏性以抑制其影响。在仿真与实测数据上的实验表明，无论是在真实复杂场景还是编解码器不匹配条件下，AnyRIR均显著优于基于L2的时域与频域反卷积方法，为AR/VR等应用提供了鲁棒的RIR估计能力。
原文摘要 · Abstract (English)
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.
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