# Improved Speech Enhancement with the Wave-U-Net

arXiv: https://arxiv.org/abs/1811.11307 · HTML: https://mlsanity.com/paper/1811.11307/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2018.

## Built on by (most cited first, 23 of 23)

- [Music Source Separation with Band-split RNN](https://mlsanity.com/paper/2209.15174/) (2022)
- [CMGAN: Conformer-based Metric GAN for Speech Enhancement](https://mlsanity.com/paper/2203.15149/) (2022)
- [Separate Anything You Describe](https://mlsanity.com/paper/2308.05037/) (2023)
- [TSTNN: Two-stage Transformer based Neural Network for Speech Enhancement in the Time Domain](https://mlsanity.com/paper/2103.09963/) (2021)
- [CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancement](https://mlsanity.com/paper/2209.11112/) (2022)
- [Spiking Structured State Space Model for Monaural Speech Enhancement](https://mlsanity.com/paper/2309.03641/) (2023)
- [L3DAS21 Challenge: Machine Learning for 3D Audio Signal Processing](https://mlsanity.com/paper/2104.05499/) (2021)
- [Self-Attention for Audio Super-Resolution](https://mlsanity.com/paper/2108.11637/) (2021)
- [A Multi-dimensional Deep Structured State Space Approach to Speech Enhancement Using Small-footprint Models](https://mlsanity.com/paper/2306.00331/) (2023)
- [Single Channel Speech Enhancement Using U-Net Spiking Neural Networks](https://mlsanity.com/paper/2307.14464/) (2023)
- [DPSNN: Spiking Neural Network for Low-Latency Streaming Speech Enhancement](https://mlsanity.com/paper/2408.07388/) (2024)
- [Adversarial Audio Synthesis with Complex-valued Polynomial Networks](https://mlsanity.com/paper/2206.06811/) (2022)
- [ROSE: A Recognition-Oriented Speech Enhancement Framework in Air Traffic Control Using Multi-Objective Learning](https://mlsanity.com/paper/2312.06118/) (2023)
- [MS-HuBERT: Mitigating Pre-training and Inference Mismatch in Masked Language Modelling methods for learning Speech Representations](https://mlsanity.com/paper/2406.05661/) (2024)
- [Audio Denoising for Robust Audio Fingerprinting](https://mlsanity.com/paper/2212.11277/) (2022)
- [Complex-Cycle-Consistent Diffusion Model for Monaural Speech Enhancement](https://mlsanity.com/paper/2412.08856/) (2024)
- [Speech Enhancement using Separable Polling Attention and Global Layer Normalization followed with PReLU](https://mlsanity.com/paper/2105.02509/) (2021)
- [Improving the Intent Classification accuracy in Noisy Environment](https://mlsanity.com/paper/2303.06585/) (2023)
- [Music Augmentation and Denoising For Peak-Based Audio Fingerprinting](https://mlsanity.com/paper/2310.13388/) (2023)
- [Speech Bandwidth Expansion Via High Fidelity Generative Adversarial Networks](https://mlsanity.com/paper/2407.18571/) (2024)
- [MAJL: A Model-Agnostic Joint Learning Framework for Music Source Separation and Pitch Estimation](https://mlsanity.com/paper/2501.03689/) (2025)
- [A Comparative Evaluation of Deep Learning Models for Speech Enhancement in Real-World Noisy Environments](https://mlsanity.com/paper/2506.15000/) (2025)
- [ADNAC: Audio Denoiser using Neural Audio Codec](https://mlsanity.com/paper/2511.01773/) (2025)

## Abstract (arXiv)

We study the use of the Wave-U-Net architecture for speech enhancement, a model introduced by Stoller et al for the separation of music vocals and accompaniment. This end-to-end learning method for audio source separation operates directly in the time domain, permitting the integrated modelling of phase information and being able to take large temporal contexts into account. Our experiments show that the proposed method improves several metrics, namely PESQ, CSIG, CBAK, COVL and SSNR, over the state-of-the-art with respect to the speech enhancement task on the Voice Bank corpus (VCTK) dataset. We find that a reduced number of hidden layers is sufficient for speech enhancement in comparison to the original system designed for singing voice separation in music. We see this initial result as an encouraging signal to further explore speech enhancement in the time-domain, both as an end in itself and as a pre-processing step to speech recognition systems.