# D'er'everb'eration non-supervis'ee de la parole par mod`ele hybride

arXiv: https://arxiv.org/abs/2510.09025 · HTML: https://mlsanity.com/paper/2510.09025/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 10 Oct 2025.

## Builds on (most cited first, 6 of 6)

- [Pyroomacoustics: A Python package for audio room simulations and array processing algorithms](https://mlsanity.com/paper/1710.04196/) (2017)
- [FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement](https://mlsanity.com/paper/2010.15508/) (2020)
- [TF-GridNet: Integrating Full- and Sub-Band Modeling for Speech Separation](https://mlsanity.com/paper/2211.12433/) (2022)
- [Speech Dereverberation with A Reverberation Time Shortening Target](https://mlsanity.com/paper/2204.08765/) (2022)
- [BUDDy: Single-Channel Blind Unsupervised Dereverberation with Diffusion Models](https://mlsanity.com/paper/2405.04272/) (2024)
- [USDnet: Unsupervised Speech Dereverberation via Neural Forward Filtering](https://mlsanity.com/paper/2402.00820/) (2024)

## Abstract (arXiv)

This paper introduces a new training strategy to improve speech dereverberation systems in an unsupervised manner using only reverberant speech. Most existing algorithms rely on paired dry/reverberant data, which is difficult to obtain. Our approach uses limited acoustic information, like the reverberation time (RT60), to train a dereverberation system. Experimental results demonstrate that our method achieves more consistent performance across various objective metrics than the state-of-the-art.