mlsanityData to 20 Oct 2026

Subjective Evaluation of Deep Neural Network Based Speech Enhancement Systems in Real-World Conditions

arXiv 2208.05057 ↗First posted 9 Aug 2022cs.SD cs.MM eess.AScited by 0

On the map: Speech and audio language models → Speech enhancement and noise suppression

Abstract (arXiv)

Subjective evaluation results for two low-latency deep neural networks (DNN) are compared to a matured version of a traditional Wiener-filter based noise suppressor. The target use-case is real-world single-channel speech enhancement applications, e.g., communications. Real-world recordings consisting of additive stationary and non-stationary noise types are included. The evaluation is divided into four outcomes: speech quality, noise transparency, speech intelligibility or listening effort, and noise level w.r.t. speech. It is shown that DNNs improve noise suppression in all conditions in comparison to the traditional Wiener-filter baseline without major degradation in speech quality and noise transparency while maintaining speech intelligibility better than the baseline.

Key facts. No citing papers in the map yet (data through 20 Oct 2026). Placed in Speech enhancement and noise suppression, one of 11 directions in Speech and audio language models. Builds on 14 of its 42 references that are in the map. First posted on arXiv 9 Aug 2022.

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14 of 42 references.

Closest ancestors · 2

Same direction, newest first

  1. 2021Test-Time Adaptation Toward Personalized Speech Enhancement: Zero-Shot Learning with Knowledge Distillation8 May
  2. 2021INTERSPEECH 2021 ConferencingSpeech Challenge: Towards Far-field Multi-Channel Speech Enhancement for Video Conferencing2 Apr

Other references · 12

  1. 2014Adam: A Method for Stochastic Optimizationcited by 38,262
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,838
  3. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,433
  4. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,350
  5. 2020DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancementcited by 284
  6. 2020Real Time Speech Enhancement in the Waveform Domaincited by 282
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  8. 2020The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Resultscited by 230
  9. 2020FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancementcited by 121
  10. 2019Phase-aware Speech Enhancement with Deep Complex U-Netcited by 110
  11. 2022ICASSP 2022 Deep Noise Suppression Challengecited by 107
  12. 2021Deep neural network Based Low-latency Speech Separation with Asymmetric analysis-Synthesis Window Paircited by 10

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