# A Flow-Based Neural Network for Time Domain Speech Enhancement

arXiv: https://arxiv.org/abs/2106.09008 · HTML: https://mlsanity.com/paper/2106.09008/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2021.

## Built on by (most cited first, 16 of 16)

- [Conditional Diffusion Probabilistic Model for Speech Enhancement](https://mlsanity.com/paper/2202.05256/) (2022)
- [Universal Speech Enhancement with Score-based Diffusion](https://mlsanity.com/paper/2206.03065/) (2022)
- [A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AI](https://mlsanity.com/paper/2303.13336/) (2023)
- [A Study on Speech Enhancement Based on Diffusion Probabilistic Model](https://mlsanity.com/paper/2107.11876/) (2021)
- [Cold Diffusion for Speech Enhancement](https://mlsanity.com/paper/2211.02527/) (2022)
- [SRTNet: Time Domain Speech Enhancement Via Stochastic Refinement](https://mlsanity.com/paper/2210.16805/) (2022)
- [Metric-oriented Speech Enhancement using Diffusion Probabilistic Model](https://mlsanity.com/paper/2302.11989/) (2023)
- [Noise-aware Speech Enhancement using Diffusion Probabilistic Model](https://mlsanity.com/paper/2307.08029/) (2023)
- [SE-Bridge: Speech Enhancement with Consistent Brownian Bridge](https://mlsanity.com/paper/2305.13796/) (2023)
- [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)
- [Diff-SV: A Unified Hierarchical Framework for Noise-Robust Speaker Verification Using Score-Based Diffusion Probabilistic Models](https://mlsanity.com/paper/2309.08320/) (2023)
- [Diffusion Gaussian Mixture Audio Denoise](https://mlsanity.com/paper/2406.09154/) (2024)
- [INN-PAR: Invertible Neural Network for PPG to ABP Reconstruction](https://mlsanity.com/paper/2409.09021/) (2024)
- [RestoreGrad: Signal Restoration Using Conditional Denoising Diffusion Models with Jointly Learned Prior](https://mlsanity.com/paper/2502.13574/) (2025)
- [EffiFusion-GAN: Efficient Fusion Generative Adversarial Network for Speech Enhancement](https://mlsanity.com/paper/2508.14525/) (2025)

## Abstract (arXiv)

Speech enhancement involves the distinction of a target speech signal from an intrusive background. Although generative approaches using Variational Autoencoders or Generative Adversarial Networks (GANs) have increasingly been used in recent years, normalizing flow (NF) based systems are still scarse, despite their success in related fields. Thus, in this paper we propose a NF framework to directly model the enhancement process by density estimation of clean speech utterances conditioned on their noisy counterpart. The WaveGlow model from speech synthesis is adapted to enable direct enhancement of noisy utterances in time domain. In addition, we demonstrate that nonlinear input companding benefits the model performance by equalizing the distribution of input samples. Experimental evaluation on a publicly available dataset shows comparable results to current state-of-the-art GAN-based approaches, while surpassing the chosen baselines using objective evaluation metrics.