# Speech Dereverberation with A Reverberation Time Shortening Target

arXiv: https://arxiv.org/abs/2204.08765 · HTML: https://mlsanity.com/paper/2204.08765/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2022.

## Built on by (most cited first, 10 of 10)

- [Speech enhancement with frequency domain auto-regressive modeling](https://mlsanity.com/paper/2309.13537/) (2023)
- [Extending DNN-based Multiplicative Masking to Deep Subband Filtering for Improved Dereverberation](https://mlsanity.com/paper/2303.00529/) (2023)
- [A Hybrid Model for Weakly-Supervised Speech Dereverberation](https://mlsanity.com/paper/2502.06839/) (2025)
- [CleanMel: Mel-Spectrogram Enhancement for Improving Both Speech Quality and ASR](https://mlsanity.com/paper/2502.20040/) (2025)
- [Speech Synthesis By Unrolling Diffusion Process using Neural Network Layers](https://mlsanity.com/paper/2309.09652/) (2023)
- [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)
- [How much to Dereverberate? Low-Latency Single-Channel Speech Enhancement in Distant Microphone Scenarios](https://mlsanity.com/paper/2505.01338/) (2025)
- [U-DREAM: Unsupervised Dereverberation guided by a Reverberation Model](https://mlsanity.com/paper/2507.14237/) (2025)
- [D'er'everb'eration non-supervis'ee de la parole par mod`ele hybride](https://mlsanity.com/paper/2510.09025/) (2025)
- [StuPASE: Towards Low-Hallucination Studio-Quality Generative Speech Enhancement](https://mlsanity.com/paper/2603.09234/) (2026)

## Abstract (arXiv)

This work proposes a new learning target based on reverberation time shortening (RTS) for speech dereverberation. The learning target for dereverberation is usually set as the direct-path speech or optionally with some early reflections. This type of target suddenly truncates the reverberation, and thus it may not be suitable for network training. The proposed RTS target suppresses reverberation and meanwhile maintains the exponential decaying property of reverberation, which will ease the network training, and thus reduce signal distortion caused by the prediction error. Moreover, this work experimentally study to adapt our previously proposed FullSubNet speech denoising network to speech dereverberation. Experiments show that RTS is a more suitable learning target than direct-path speech and early reflections, in terms of better suppressing reverberation and signal distortion. FullSubNet is able to achieve outstanding dereverberation performance.