AnyRIR: Robust Non-intrusive Room Impulse Response Estimation in the Wild
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908773907431424 |
|---|---|
| author | Lee, Kyung Yun Meyer-Kahlen, Nils Prawda, Karolina Välimäki, Vesa Schlecht, Sebastian J. |
| author_facet | Lee, Kyung Yun Meyer-Kahlen, Nils Prawda, Karolina Välimäki, Vesa Schlecht, Sebastian J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_17788 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AnyRIR: Robust Non-intrusive Room Impulse Response Estimation in the Wild Lee, Kyung Yun Meyer-Kahlen, Nils Prawda, Karolina Välimäki, Vesa Schlecht, Sebastian J. Audio and Speech Processing 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. |
| title | AnyRIR: Robust Non-intrusive Room Impulse Response Estimation in the Wild |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2510.17788 |