# Ultra Low Complexity Deep Learning Based Noise Suppression

arXiv: https://arxiv.org/abs/2312.08132 · HTML: https://mlsanity.com/paper/2312.08132/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 31 references that are in the map.
- First posted on arXiv 13 Dec 2023.

## Builds on (most cited first, 19 of 19)

- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [DNSMOS: A Non-Intrusive Perceptual Objective Speech Quality metric to evaluate Noise Suppressors](https://mlsanity.com/paper/2010.15258/) (2020)
- [SEGAN: Speech Enhancement Generative Adversarial Network](https://mlsanity.com/paper/1703.09452/) (2017)
- [DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement](https://mlsanity.com/paper/2008.00264/) (2020)
- [The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results](https://mlsanity.com/paper/2005.13981/) (2020)
- [DeepFilterNet: A Low Complexity Speech Enhancement Framework for Full-Band Audio based on Deep Filtering](https://mlsanity.com/paper/2110.05588/) (2021)
- [Towards efficient models for real-time deep noise suppression](https://mlsanity.com/paper/2101.09249/) (2021)
- [FullSubNet+: Channel Attention FullSubNet with Complex Spectrograms for Speech Enhancement](https://mlsanity.com/paper/2203.12188/) (2022)
- [FRCRN: Boosting Feature Representation using Frequency Recurrence for Monaural Speech Enhancement](https://mlsanity.com/paper/2206.07293/) (2022)
- [DeepFilterNet2: Towards Real-Time Speech Enhancement on Embedded Devices for Full-Band Audio](https://mlsanity.com/paper/2205.05474/) (2022)
- [A Perceptually-Motivated Approach for Low-Complexity, Real-Time Enhancement of Fullband Speech](https://mlsanity.com/paper/2008.04259/) (2020)
- [Data augmentation and loss normalization for deep noise suppression](https://mlsanity.com/paper/2008.06412/) (2020)
- [Real-time Denoising and Dereverberation with Tiny Recurrent U-Net](https://mlsanity.com/paper/2102.03207/) (2021)
- [Deep Filtering: Signal Extraction and Reconstruction Using Complex Time-Frequency Filters](https://mlsanity.com/paper/1904.08369/) (2019)
- [Channel-wise Subband Input for Better Voice and Accompaniment Separation on High Resolution Music](https://mlsanity.com/paper/2008.05216/) (2020)
- [CLCNet: Deep learning-based Noise Reduction for Hearing Aids using Complex Linear Coding](https://mlsanity.com/paper/2001.10218/) (2020)
- [Phase-aware Single-stage Speech Denoising and Dereverberation with U-Net](https://mlsanity.com/paper/2006.00687/) (2020)
- [DMF-Net: A decoupling-style multi-band fusion model for full-band speech enhancement](https://mlsanity.com/paper/2203.00472/) (2022)

## Built on by (most cited first, 7 of 7)

- [Modulating State Space Model with SlowFast Framework for Compute-Efficient Ultra Low-Latency Speech Enhancement](https://mlsanity.com/paper/2411.02019/) (2024)
- [Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement](https://mlsanity.com/paper/2508.20859/) (2025)
- [Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns](https://mlsanity.com/paper/2509.05079/) (2025)
- [Fast-ULCNet: A fast and ultra low complexity network for single-channel speech enhancement](https://mlsanity.com/paper/2601.14925/) (2026)
- [DFingerNet: Noise-Adaptive Speech Enhancement for Hearing Aids](https://mlsanity.com/paper/2501.10525/) (2025)
- [SlimDiffuSE: Towards Efficient Diffusion-Based Speech Enhancement using Slimmable Networks](https://mlsanity.com/paper/2608.21188/) (2026)
- [Discriminative Flow Matching: Beyond Time-Conditioning in Generative Restoration via Flow-State Representations](https://mlsanity.com/paper/2609.04525/) (2026)

## 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.