# A Hybrid Model for Weakly-Supervised Speech Dereverberation

arXiv: https://arxiv.org/abs/2502.06839 · HTML: https://mlsanity.com/paper/2502.06839/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 41 references that are in the map.
- First posted on arXiv 6 Feb 2025.

## Builds on (most cited first, 20 of 20)

- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [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)
- [A Recurrent Variational Autoencoder for Speech Enhancement](https://mlsanity.com/paper/1910.10942/) (2019)
- [Analysing Diffusion-based Generative Approaches versus Discriminative Approaches for Speech Restoration](https://mlsanity.com/paper/2211.02397/) (2022)
- [Variational Autoencoder for Speech Enhancement with a Noise-Aware Encoder](https://mlsanity.com/paper/2102.08706/) (2021)
- [MetricGAN-U: Unsupervised speech enhancement/ dereverberation based only on noisy/ reverberated speech](https://mlsanity.com/paper/2110.05866/) (2021)
- [Unsupervised vocal dereverberation with diffusion-based generative models](https://mlsanity.com/paper/2211.04124/) (2022)
- [The PESQetarian: On the Relevance of Goodhart's Law for Speech Enhancement](https://mlsanity.com/paper/2406.03460/) (2024)
- [Speech Dereverberation with A Reverberation Time Shortening Target](https://mlsanity.com/paper/2204.08765/) (2022)
- [Diffusion Posterior Sampling for Informed Single-Channel Dereverberation](https://mlsanity.com/paper/2306.12286/) (2023)
- [BUDDy: Single-Channel Blind Unsupervised Dereverberation with Diffusion Models](https://mlsanity.com/paper/2405.04272/) (2024)
- [CycleGAN-based Non-parallel Speech Enhancement with an Adaptive Attention-in-attention Mechanism](https://mlsanity.com/paper/2107.13143/) (2021)
- [Convolutive Prediction for Monaural Speech Dereverberation and Noisy-Reverberant Speaker Separation](https://mlsanity.com/paper/2108.07376/) (2021)
- [USDnet: Unsupervised Speech Dereverberation via Neural Forward Filtering](https://mlsanity.com/paper/2402.00820/) (2024)
- [RVAE-EM: Generative speech dereverberation based on recurrent variational auto-encoder and convolutive transfer function](https://mlsanity.com/paper/2309.08157/) (2023)
- [CycleGAN-Based Unpaired Speech Dereverberation](https://mlsanity.com/paper/2203.15652/) (2022)
- [Speech dereverberation constrained on room impulse response characteristics](https://mlsanity.com/paper/2407.08657/) (2024)
- [A Composite T60 Regression and Classification Approach for Speech Dereverberation](https://mlsanity.com/paper/2302.04932/) (2023)

## Built on by (most cited first, 2 of 2)

- [U-DREAM: Unsupervised Dereverberation guided by a Reverberation Model](https://mlsanity.com/paper/2507.14237/) (2025)
- [Is Phase Really Needed for Weakly-Supervised Dereverberation ?](https://mlsanity.com/paper/2511.17346/) (2025)

## Abstract (arXiv)

This paper introduces a new training strategy to improve speech dereverberation systems using minimal acoustic information and reverberant (wet) speech. Most existing algorithms rely on paired dry/wet data, which is difficult to obtain, or on target metrics that may not adequately capture reverberation characteristics and can lead to poor results on non-target metrics. Our approach uses limited acoustic information, like the reverberation time (RT60), to train a dereverberation system. The system's output is resynthesized using a generated room impulse response and compared with the original reverberant speech, providing a novel reverberation matching loss replacing the standard target metrics. During inference, only the trained dereverberation model is used. Experimental results demonstrate that our method achieves more consistent performance across various objective metrics used in speech dereverberation than the state-of-the-art.