Is Phase Really Needed for Weakly-Supervised Dereverberation ?
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_ | 1866917096987820032 |
|---|---|
| author | Rodrigues, Marius Bahrman, Louis Badeau, Roland Richard, Gaël |
| author_facet | Rodrigues, Marius Bahrman, Louis Badeau, Roland Richard, Gaël |
| contents | In unsupervised or weakly-supervised approaches for speech dereverberation, the target clean (dry) signals are considered to be unknown during training. In that context, evaluating to what extent information can be retrieved from the sole knowledge of reverberant (wet) speech becomes critical. This work investigates the role of the reverberant (wet) phase in the time-frequency domain. Based on Statistical Wave Field Theory, we show that late reverberation perturbs phase components with white, uniformly distributed noise, except at low frequencies. Consequently, the wet phase carries limited useful information and is not essential for weakly supervised dereverberation. To validate this finding, we train dereverberation models under a recent weak supervision framework and demonstrate that performance can be significantly improved by excluding the reverberant phase from the loss function. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_17346 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Is Phase Really Needed for Weakly-Supervised Dereverberation ? Rodrigues, Marius Bahrman, Louis Badeau, Roland Richard, Gaël Sound Artificial Intelligence Signal Processing Classical Physics Machine Learning In unsupervised or weakly-supervised approaches for speech dereverberation, the target clean (dry) signals are considered to be unknown during training. In that context, evaluating to what extent information can be retrieved from the sole knowledge of reverberant (wet) speech becomes critical. This work investigates the role of the reverberant (wet) phase in the time-frequency domain. Based on Statistical Wave Field Theory, we show that late reverberation perturbs phase components with white, uniformly distributed noise, except at low frequencies. Consequently, the wet phase carries limited useful information and is not essential for weakly supervised dereverberation. To validate this finding, we train dereverberation models under a recent weak supervision framework and demonstrate that performance can be significantly improved by excluding the reverberant phase from the loss function. |
| title | Is Phase Really Needed for Weakly-Supervised Dereverberation ? |
| topic | Sound Artificial Intelligence Signal Processing Classical Physics Machine Learning |
| url | https://arxiv.org/abs/2511.17346 |