# μNet: Ultra-Low-Memory and Low-Complexity Speech Enhancement for Embedded Digital Signal Processors

arXiv: https://arxiv.org/abs/2608.21155 · HTML: https://mlsanity.com/paper/2608.21155/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Speech enhancement and noise suppression, one of 11 directions in Speech and audio language models.
- Builds on 27 of its 40 references that are in the map.
- First posted on arXiv 21 Aug 2026.

## Placed in

- Speech and audio language models / [Speech enhancement and noise suppression](https://mlsanity.com/speech/speech-enhancement-and-noise-suppression/)

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation](https://mlsanity.com/paper/1809.07454/) (2018)
- [DNSMOS P.835: A Non-Intrusive Perceptual Objective Speech Quality Metric to Evaluate Noise Suppressors](https://mlsanity.com/paper/2110.01763/) (2021)
- [DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement](https://mlsanity.com/paper/2008.00264/) (2020)
- [Parallel WaveNet: Fast High-Fidelity Speech Synthesis](https://mlsanity.com/paper/1711.10433/) (2017)
- [The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results](https://mlsanity.com/paper/2005.13981/) (2020)
- [TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems](https://mlsanity.com/paper/2010.08678/) (2020)
- [A Hybrid DSP/Deep Learning Approach to Real-Time Full-Band Speech Enhancement](https://mlsanity.com/paper/1709.08243/) (2017)
- [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)
- [A Perceptually-Motivated Approach for Low-Complexity, Real-Time Enhancement of Fullband Speech](https://mlsanity.com/paper/2008.04259/) (2020)
- [DeepFilterNet2: Towards Real-Time Speech Enhancement on Embedded Devices for Full-Band Audio](https://mlsanity.com/paper/2205.05474/) (2022)
- [Real-time Denoising and Dereverberation with Tiny Recurrent U-Net](https://mlsanity.com/paper/2102.03207/) (2021)
- [STFT-Domain Neural Speech Enhancement with Very Low Algorithmic Latency](https://mlsanity.com/paper/2204.09911/) (2022)
- [Channel-wise Subband Input for Better Voice and Accompaniment Separation on High Resolution Music](https://mlsanity.com/paper/2008.05216/) (2020)
- [Rethinking Processing Distortions: Disentangling the Impact of Speech Enhancement Errors on Speech Recognition Performance](https://mlsanity.com/paper/2404.14860/) (2024)
- [Ultra Low Complexity Deep Learning Based Noise Suppression](https://mlsanity.com/paper/2312.08132/) (2023)
- [Deep neural network Based Low-latency Speech Separation with Asymmetric analysis-Synthesis Window Pair](https://mlsanity.com/paper/2106.11794/) (2021)
- [Modulating State Space Model with SlowFast Framework for Compute-Efficient Ultra Low-Latency Speech Enhancement](https://mlsanity.com/paper/2411.02019/) (2024)
- [Low-latency Monaural Speech Enhancement with Deep Filter-bank Equalizer](https://mlsanity.com/paper/2202.06764/) (2022)
- [A Hybrid Approach for Low-Complexity Joint Acoustic Echo and Noise Reduction](https://mlsanity.com/paper/2408.15746/) (2024)
- [Ultra-Low Latency Speech Enhancement - A Comprehensive Study](https://mlsanity.com/paper/2409.10358/) (2024)
- [Align-ULCNet: Towards Low-Complexity and Robust Acoustic Echo and Noise Reduction](https://mlsanity.com/paper/2410.13620/) (2024)
- [Fast-ULCNet: A fast and ultra low complexity network for single-channel speech enhancement](https://mlsanity.com/paper/2601.14925/) (2026)
- [A two-step approach for speech enhancement in low-SNR scenarios using cyclostationary beamforming and DNNs](https://mlsanity.com/paper/2602.12986/) (2026)

## Abstract (arXiv)

Speech enhancement on embedded digital signal processors (DSPs) imposes strict constraints on memory footprint, computational complexity, latency, and support for integer operations. Although recent DNN-based approaches have addressed these challenges individually, no unified framework in the literature simultaneously addresses all these requirements for practical deployment. In this work, we propose μNet, an ultra-low-memory, low-complexity, and low-latency end-to-end DNN model. The proposed method requires only 90~KB of static memory and 28~MMACs, while supporting an algorithmic latency as low as 4~ms with performance comparable to state-of-the-art methods of similar complexity. Our experiments demonstrate that μNet is compatible with neural accelerators and supports full integer-arithmetic operations on consumer DSP platforms such as Cadence Tensilica HiFi 4/5.