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Improved Speech Enhancement with the Wave-U-Net

arXiv 1811.11307 ↗First posted 27 Nov 2018cs.SD cs.LG cs.NEcited by 23

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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.

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

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Citing papers per year: 2021: 4; 2022: 5; 2023: 7; 2024: 4; 2025: 3.

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  1. 2022Music Source Separation with Band-split RNNcited by 70
  2. 2022CMGAN: Conformer-based Metric GAN for Speech Enhancementcited by 45
  3. 2023Separate Anything You Describecited by 43
  4. 2021TSTNN: Two-stage Transformer based Neural Network for Speech Enhancement in the Time Domaincited by 36
  5. 2022CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancementcited by 23
  6. 2023Spiking Structured State Space Model for Monaural Speech Enhancementcited by 23
  7. 2021L3DAS21 Challenge: Machine Learning for 3D Audio Signal Processingcited by 9
  8. 2021Self-Attention for Audio Super-Resolutioncited by 9
  9. 2023A Multi-dimensional Deep Structured State Space Approach to Speech Enhancement Using Small-footprint Modelscited by 9
  10. 2023Single Channel Speech Enhancement Using U-Net Spiking Neural Networkscited by 5
  11. 2024DPSNN: Spiking Neural Network for Low-Latency Streaming Speech Enhancementcited by 5
  12. 2022Adversarial Audio Synthesis with Complex-valued Polynomial Networkscited by 3
11 more
  1. 2023ROSE: A Recognition-Oriented Speech Enhancement Framework in Air Traffic Control Using Multi-Objective Learningcited by 2
  2. 2024MS-HuBERT: Mitigating Pre-training and Inference Mismatch in Masked Language Modelling methods for learning Speech Representationscited by 2
  3. 2022Audio Denoising for Robust Audio Fingerprintingcited by 1
  4. 2024Complex-Cycle-Consistent Diffusion Model for Monaural Speech Enhancementcited by 1
  5. 2021Speech Enhancement using Separable Polling Attention and Global Layer Normalization followed with PReLUcited by 0
  6. 2023Improving the Intent Classification accuracy in Noisy Environmentcited by 0
  7. 2023Music Augmentation and Denoising For Peak-Based Audio Fingerprintingcited by 0
  8. 2024Speech Bandwidth Expansion Via High Fidelity Generative Adversarial Networkscited by 0
  9. 2025MAJL: A Model-Agnostic Joint Learning Framework for Music Source Separation and Pitch Estimationcited by 0
  10. 2025A Comparative Evaluation of Deep Learning Models for Speech Enhancement in Real-World Noisy Environmentscited by 0
  11. 2025ADNAC: Audio Denoiser using Neural Audio Codeccited by 0