# SRTNet: Time Domain Speech Enhancement Via Stochastic Refinement

arXiv: https://arxiv.org/abs/2210.16805 · HTML: https://mlsanity.com/paper/2210.16805/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Audio, speech and music, one of 14 directions in Diffusion models.
- Builds on 22 of its 35 references that are in the map.
- First posted on arXiv 30 Oct 2022.

## Placed in

- Diffusion models / [Audio, speech and music](https://mlsanity.com/diffusion/audio-speech-and-music/)

## Builds on (most cited first, 22 of 22)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [DiffWave: A Versatile Diffusion Model for Audio Synthesis](https://mlsanity.com/paper/2009.09761/) (2020)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [WaveGrad: Estimating Gradients for Waveform Generation](https://mlsanity.com/paper/2009.00713/) (2020)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation](https://mlsanity.com/paper/1809.07454/) (2018)
- [Deblurring via Stochastic Refinement](https://mlsanity.com/paper/2112.02475/) (2021)
- [Real Time Speech Enhancement in the Waveform Domain](https://mlsanity.com/paper/2006.12847/) (2020)
- [SEGAN: Speech Enhancement Generative Adversarial Network](https://mlsanity.com/paper/1703.09452/) (2017)
- [Conditional Diffusion Probabilistic Model for Speech Enhancement](https://mlsanity.com/paper/2202.05256/) (2022)
- [MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement](https://mlsanity.com/paper/1905.04874/) (2019)
- [A Study on Speech Enhancement Based on Diffusion Probabilistic Model](https://mlsanity.com/paper/2107.11876/) (2021)
- [A Recurrent Variational Autoencoder for Speech Enhancement](https://mlsanity.com/paper/1910.10942/) (2019)
- [Statistical Speech Enhancement Based on Probabilistic Integration of Variational Autoencoder and Non-Negative Matrix Factorization](https://mlsanity.com/paper/1710.11439/) (2017)
- [A variance modeling framework based on variational autoencoders for speech enhancement](https://mlsanity.com/paper/1902.01605/) (2019)
- [Improving GANs for Speech Enhancement](https://mlsanity.com/paper/2001.05532/) (2020)
- [Exploring Speech Enhancement with Generative Adversarial Networks for Robust Speech Recognition](https://mlsanity.com/paper/1711.05747/) (2017)
- [A Flow-Based Neural Network for Time Domain Speech Enhancement](https://mlsanity.com/paper/2106.09008/) (2021)
- [Semi-supervised multichannel speech enhancement with variational autoencoders and non-negative matrix factorization](https://mlsanity.com/paper/1811.06713/) (2018)
- [WaveCRN: An Efficient Convolutional Recurrent Neural Network for End-to-end Speech Enhancement](https://mlsanity.com/paper/2004.04098/) (2020)
- [Mixture of Inference Networks for VAE-based Audio-visual Speech Enhancement](https://mlsanity.com/paper/1912.10647/) (2019)
- [Speech enhancement with variational autoencoders and alpha-stable distributions](https://mlsanity.com/paper/1902.03926/) (2019)

## Built on by (most cited first, 12 of 12)

- [Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model](https://mlsanity.com/paper/2304.13731/) (2023)
- [StoRM: A Diffusion-based Stochastic Regeneration Model for Speech Enhancement and Dereverberation](https://mlsanity.com/paper/2212.11851/) (2022)
- [Investigating the Design Space of Diffusion Models for Speech Enhancement](https://mlsanity.com/paper/2312.04370/) (2023)
- [Modality-Invariant Bidirectional Temporal Representation Distillation Network for Missing Multimodal Sentiment Analysis](https://mlsanity.com/paper/2501.05474/) (2025)
- [Variance-Preserving-Based Interpolation Diffusion Models for Speech Enhancement](https://mlsanity.com/paper/2306.08527/) (2023)
- [A Variance-Preserving Interpolation Approach for Diffusion Models with Applications to Single Channel Speech Enhancement and Recognition](https://mlsanity.com/paper/2405.16952/) (2024)
- [A Composite Predictive-Generative Approach to Monaural Universal Speech Enhancement](https://mlsanity.com/paper/2505.24576/) (2025)
- [The Effect of Training Dataset Size on Discriminative and Diffusion-Based Speech Enhancement Systems](https://mlsanity.com/paper/2406.06160/) (2024)
- [Regularized Schr"odinger Bridge via Distortion-Perception Perturbation for High-Fidelity Speech Enhancement](https://mlsanity.com/paper/2511.11686/) (2025)
- [Magnitude-Phase Dual-Path Speech Enhancement Network based on Self-Supervised Embedding and Perceptual Contrast Stretch Boosting](https://mlsanity.com/paper/2503.21571/) (2025)
- [Dual-View Predictive Diffusion: Lightweight Speech Enhancement via Spectrogram-Image Synergy](https://mlsanity.com/paper/2602.00568/) (2026)
- [DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement](https://mlsanity.com/paper/2606.05911/) (2026)

## Abstract (arXiv)

Diffusion model, as a new generative model which is very popular in image generation and audio synthesis, is rarely used in speech enhancement. In this paper, we use the diffusion model as a module for stochastic refinement. We propose SRTNet, a novel method for speech enhancement via Stochastic Refinement in complete Time domain. Specifically, we design a joint network consisting of a deterministic module and a stochastic module, which makes up the ``enhance-and-refine'' paradigm. We theoretically demonstrate the feasibility of our method and experimentally prove that our method achieves faster training, faster sampling and higher quality. Our code and enhanced samples are available at https://github.com/zhibinQiu/SRTNet.git.