U-DREAM: Unsupervised Dereverberation guided by a Reverberation Model
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910074437369856 |
|---|---|
| author | Bahrman, Louis Rodrigues, Marius Fontaine, Mathieu Richard, Gaël |
| author_facet | Bahrman, Louis Rodrigues, Marius Fontaine, Mathieu Richard, Gaël |
| contents | This paper explores the outcome of training state-of-the-art dereverberation models with supervision settings ranging from weakly-supervised to virtually unsupervised, relying solely on reverberant signals and an acoustic model for training. Most of the existing deep learning approaches typically require paired dry and reverberant data, which are difficult to obtain in practice. We develop instead a sequential learning strategy motivated by a maximum-likelihood formulation of the dereverberation problem, wherein acoustic parameters and dry signals are estimated from reverberant inputs using deep neural networks, guided by a reverberation matching loss. Our most data-efficient variant requires only 100 reverberation-parameter-labeled samples to outperform an unsupervised baseline, demonstrating the effectiveness and practicality of the proposed method in low-resource scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_14237 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | U-DREAM: Unsupervised Dereverberation guided by a Reverberation Model Bahrman, Louis Rodrigues, Marius Fontaine, Mathieu Richard, Gaël Sound Artificial Intelligence Audio and Speech Processing Signal Processing This paper explores the outcome of training state-of-the-art dereverberation models with supervision settings ranging from weakly-supervised to virtually unsupervised, relying solely on reverberant signals and an acoustic model for training. Most of the existing deep learning approaches typically require paired dry and reverberant data, which are difficult to obtain in practice. We develop instead a sequential learning strategy motivated by a maximum-likelihood formulation of the dereverberation problem, wherein acoustic parameters and dry signals are estimated from reverberant inputs using deep neural networks, guided by a reverberation matching loss. Our most data-efficient variant requires only 100 reverberation-parameter-labeled samples to outperform an unsupervised baseline, demonstrating the effectiveness and practicality of the proposed method in low-resource scenarios. |
| title | U-DREAM: Unsupervised Dereverberation guided by a Reverberation Model |
| topic | Sound Artificial Intelligence Audio and Speech Processing Signal Processing |
| url | https://arxiv.org/abs/2507.14237 |