# RVAE-EM: Generative speech dereverberation based on recurrent variational auto-encoder and convolutive transfer function

arXiv: https://arxiv.org/abs/2309.08157 · HTML: https://mlsanity.com/paper/2309.08157/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2023.

## Built on by (most cited first, 5 of 5)

- [Unsupervised Blind Joint Dereverberation and Room Acoustics Estimation with Diffusion Models](https://mlsanity.com/paper/2408.07472/) (2024)
- [ROSE: A Recognition-Oriented Speech Enhancement Framework in Air Traffic Control Using Multi-Objective Learning](https://mlsanity.com/paper/2312.06118/) (2023)
- [A Hybrid Model for Weakly-Supervised Speech Dereverberation](https://mlsanity.com/paper/2502.06839/) (2025)
- [VINP: Variational Bayesian Inference with Neural Speech Prior for Joint ASR-Effective Speech Dereverberation and Blind RIR Identification](https://mlsanity.com/paper/2502.07205/) (2025)
- [U-DREAM: Unsupervised Dereverberation guided by a Reverberation Model](https://mlsanity.com/paper/2507.14237/) (2025)

## Abstract (arXiv)

In indoor scenes, reverberation is a crucial factor in degrading the perceived quality and intelligibility of speech. In this work, we propose a generative dereverberation method. Our approach is based on a probabilistic model utilizing a recurrent variational auto-encoder (RVAE) network and the convolutive transfer function (CTF) approximation. Different from most previous approaches, the output of our RVAE serves as the prior of the clean speech. And our target is the maximum a posteriori (MAP) estimation of clean speech, which is achieved iteratively through the expectation maximization (EM) algorithm. The proposed method integrates the capabilities of network-based speech prior modelling and CTF-based observation modelling. Experiments on single-channel speech dereverberation show that the proposed generative method noticeably outperforms the advanced discriminative networks.