mlsanityData to 7 Oct 2026

Ultra Low Complexity Deep Learning Based Noise Suppression

arXiv 2312.08132 ↗First posted 13 Dec 2023eess.AS cs.LG eess.SPcited by 7

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Abstract (arXiv)

This paper introduces an innovative method for reducing the computational complexity of deep neural networks in real-time speech enhancement on resource-constrained devices. The proposed approach utilizes a two-stage processing framework, employing channelwise feature reorientation to reduce the computational load of convolutional operations. By combining this with a modified power law compression technique for enhanced perceptual quality, this approach achieves noise suppression performance comparable to state-of-the-art methods with significantly less computational requirements. Notably, our algorithm exhibits 3 to 4 times less computational complexity and memory usage than prior state-of-the-art approaches.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 19 of its 31 references that are in the map. First posted on arXiv 13 Dec 2023.

Builds on

19 of 31 references.

References · 19

  1. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
  2. 2018SDR - half-baked or well done?cited by 420
  3. 2020DNSMOS: A Non-Intrusive Perceptual Objective Speech Quality metric to evaluate Noise Suppressorscited by 235
  4. 2017SEGAN: Speech Enhancement Generative Adversarial Networkcited by 165
  5. 2020DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancementcited by 133
  6. 2020The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Resultscited by 128
  7. 2021DeepFilterNet: A Low Complexity Speech Enhancement Framework for Full-Band Audio based on Deep Filteringcited by 38
  8. 2021Towards efficient models for real-time deep noise suppressioncited by 28
  9. 2022FullSubNet+: Channel Attention FullSubNet with Complex Spectrograms for Speech Enhancementcited by 28
  10. 2022FRCRN: Boosting Feature Representation using Frequency Recurrence for Monaural Speech Enhancementcited by 24
  11. 2020A Perceptually-Motivated Approach for Low-Complexity, Real-Time Enhancement of Fullband Speechcited by 23
  12. 2020Data augmentation and loss normalization for deep noise suppressioncited by 23
7 more
  1. 2022DeepFilterNet2: Towards Real-Time Speech Enhancement on Embedded Devices for Full-Band Audiocited by 23
  2. 2021Real-time Denoising and Dereverberation with Tiny Recurrent U-Netcited by 16
  3. 2019Deep Filtering: Signal Extraction and Reconstruction Using Complex Time-Frequency Filterscited by 14
  4. 2020Channel-wise Subband Input for Better Voice and Accompaniment Separation on High Resolution Musiccited by 13
  5. 2020CLCNet: Deep learning-based Noise Reduction for Hearing Aids using Complex Linear Codingcited by 9
  6. 2020Phase-aware Single-stage Speech Denoising and Dereverberation with U-Netcited by 7
  7. 2022DMF-Net: A decoupling-style multi-band fusion model for full-band speech enhancementcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 3.

7 papers.

  1. 2024Modulating State Space Model with SlowFast Framework for Compute-Efficient Ultra Low-Latency Speech Enhancementcited by 4
  2. 2025Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancementcited by 1
  3. 2025Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patternscited by 1
  4. 2026Fast-ULCNet: A fast and ultra low complexity network for single-channel speech enhancementcited by 1
  5. 2025DFingerNet: Noise-Adaptive Speech Enhancement for Hearing Aidscited by 0
  6. 2026SlimDiffuSE: Towards Efficient Diffusion-Based Speech Enhancement using Slimmable Networkscited by 0
  7. 2026Discriminative Flow Matching: Beyond Time-Conditioning in Generative Restoration via Flow-State Representationscited by 0