# A Simultaneous Denoising and Dereverberation Framework with Target Decoupling

arXiv: https://arxiv.org/abs/2106.12743 · HTML: https://mlsanity.com/paper/2106.12743/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2021.

## Built on by (most cited first, 11 of 11)

- [MaskSR: Masked Language Model for Full-band Speech Restoration](https://mlsanity.com/paper/2406.02092/) (2024)
- [CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancement](https://mlsanity.com/paper/2209.11112/) (2022)
- [Dual-branch Attention-In-Attention Transformer for single-channel speech enhancement](https://mlsanity.com/paper/2110.06467/) (2021)
- [A Generalized Bandsplit Neural Network for Cinematic Audio Source Separation](https://mlsanity.com/paper/2309.02539/) (2023)
- [Joint Semantic Knowledge Distillation and Masked Acoustic Modeling for Full-band Speech Restoration with Improved Intelligibility](https://mlsanity.com/paper/2409.09357/) (2024)
- [Deep neural network techniques for monaural speech enhancement: state of the art analysis](https://mlsanity.com/paper/2212.00369/) (2022)
- [A Mask Free Neural Network for Monaural Speech Enhancement](https://mlsanity.com/paper/2306.04286/) (2023)
- [A Multi-Stage Triple-Path Method for Speech Separation in Noisy and Reverberant Environments](https://mlsanity.com/paper/2303.03732/) (2023)
- [SepALM: Audio Language Models Are Error Correctors for Robust Speech Separation](https://mlsanity.com/paper/2505.03273/) (2025)
- [Guided Speech Enhancement Network](https://mlsanity.com/paper/2303.07486/) (2023)
- [DFingerNet: Noise-Adaptive Speech Enhancement for Hearing Aids](https://mlsanity.com/paper/2501.10525/) (2025)

## Abstract (arXiv)

Background noise and room reverberation are regarded as two major factors to degrade the subjective speech quality. In this paper, we propose an integrated framework to address simultaneous denoising and dereverberation under complicated scenario environments. It adopts a chain optimization strategy and designs four sub-stages accordingly. In the first two stages, we decouple the multi-task learning w.r.t. complex spectrum into magnitude and phase, and only implement noise and reverberation removal in the magnitude domain. Based on the estimated priors above, we further polish the spectrum in the third stage, where both magnitude and phase information are explicitly repaired with the residual learning. Due to the data mismatch and nonlinear effect of DNNs, the residual noise often exists in the DNN-processed spectrum. To resolve the problem, we adopt a light-weight algorithm as the post-processing module to capture and suppress the residual noise in the non-active regions. In the Interspeech 2021 Deep Noise Suppression (DNS) Challenge, our submitted system ranked top-1 for the real-time track in terms of Mean Opinion Score (MOS) with ITU-T P.835 framework