mlsanityData to 14 Oct 2026

How much to Dereverberate? Low-Latency Single-Channel Speech Enhancement in Distant Microphone Scenarios

arXiv 2505.01338 ↗First posted 2 May 2025eess.AS cs.LG cs.SDcited by 0

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Abstract (arXiv)

Dereverberation is an important sub-task of Speech Enhancement (SE) to improve the signal's intelligibility and quality. However, it remains challenging because the reverberation is highly correlated with the signal. Furthermore, the single-channel SE literature has predominantly focused on rooms with short reverb times (typically under 1 second), smaller rooms (under volumes of 1000 cubic meters) and relatively short distances (up to 2 meters). In this paper, we explore real-time low-latency single-channel SE under distant microphone scenarios, such as 5 to 10 meters, and focus on conference rooms and theatres, with larger room dimensions and reverberation times. Such a setup is useful for applications such as lecture demonstrations, drama, and to enhance stage acoustics. First, we show that single-channel SE in such challenging scenarios is feasible. Second, we investigate the relationship between room volume and reverberation time, and demonstrate its importance when randomly simulating room impulse responses. Lastly, we show that for dereverberation with short decay times, preserving early reflections before decaying the transfer function of the room improves overall signal quality.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 38 references that are in the map. First posted on arXiv 2 May 2025.

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22 of 38 references.

References · 22

  1. 2020DNSMOS: A Non-Intrusive Perceptual Objective Speech Quality metric to evaluate Noise Suppressorscited by 241
  2. 2020Real Time Speech Enhancement in the Waveform Domaincited by 186
  3. 2017Pyroomacoustics: A Python package for audio room simulations and array processing algorithmscited by 132
  4. 2019Music Source Separation in the Waveform Domaincited by 99
  5. 2019WHAMR!: Noisy and Reverberant Single-Channel Speech Separationcited by 61
  6. 2022ICASSP 2022 Deep Noise Suppression Challengecited by 60
  7. 2018gpuRIR: A Python Library for Room Impulse Response Simulation with GPU Accelerationcited by 46
  8. 2022VoiceFixer: A Unified Framework for High-Fidelity Speech Restorationcited by 37
  9. 2021Towards efficient models for real-time deep noise suppressioncited by 29
  10. 2022FullSubNet+: Channel Attention FullSubNet with Complex Spectrograms for Speech Enhancementcited by 29
  11. 2020ICASSP 2021 Deep Noise Suppression Challengecited by 27
  12. 2022FRCRN: Boosting Feature Representation using Frequency Recurrence for Monaural Speech Enhancementcited by 26
10 more
  1. 2023DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancementcited by 25
  2. 2022DeepFilterNet2: Towards Real-Time Speech Enhancement on Embedded Devices for Full-Band Audiocited by 24
  3. 2020Data augmentation and loss normalization for deep noise suppressioncited by 23
  4. 2023The CHiME-7 DASR Challenge: Distant Meeting Transcription with Multiple Devices in Diverse Scenarioscited by 22
  5. 2022Speech Dereverberation with A Reverberation Time Shortening Targetcited by 10
  6. 2021TPARN: Triple-path Attentive Recurrent Network for Time-domain Multichannel Speech Enhancementcited by 6
  7. 2022FRA-RIR: Fast Random Approximation of the Image-source Methodcited by 5
  8. 2022DeFT-AN: Dense Frequency-Time Attentive Network for Multichannel Speech Enhancementcited by 5
  9. 2023ICASSP 2023 Speech Signal Improvement Challengecited by 5
  10. 2024Real-time Low-latency Music Source Separation using Hybrid Spectrogram-TasNetcited by 3

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