# Towards Ultra-Low-Power Neuromorphic Speech Enhancement with Spiking-FullSubNet arXiv: https://arxiv.org/abs/2410.04785 ยท HTML: https://mlsanity.com/paper/2410.04785/ ## Key facts - Cited by 1 papers in the map (data through 19 Oct 2026). - Placed in Speech enhancement and noise suppression, one of 11 directions in Speech and audio language models. - Builds on 30 of its 67 references that are in the map. - First posted on arXiv 7 Oct 2024. ## 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, 30 of 30) - [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015) - [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019) - [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018) - [Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks](https://mlsanity.com/paper/1706.02609/) (2017) - [DNSMOS: A Non-Intrusive Perceptual Objective Speech Quality metric to evaluate Noise Suppressors](https://mlsanity.com/paper/2010.15258/) (2020) - [Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks](https://mlsanity.com/paper/2007.05785/) (2020) - [DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement](https://mlsanity.com/paper/2008.00264/) (2020) - [Long short-term memory and learning-to-learn in networks of spiking neurons](https://mlsanity.com/paper/1803.09574/) (2018) - [MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement](https://mlsanity.com/paper/1905.04874/) (2019) - [FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement](https://mlsanity.com/paper/2010.15508/) (2020) - [TF-GridNet: Integrating Full- and Sub-Band Modeling for Speech Separation](https://mlsanity.com/paper/2211.12433/) (2022) - [Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks](https://mlsanity.com/paper/2103.12593/) (2021) - [Attention Spiking Neural Networks](https://mlsanity.com/paper/2209.13929/) (2022) - [ICASSP 2022 Deep Noise Suppression Challenge](https://mlsanity.com/paper/2202.13288/) (2022) - [DeepFilterNet: A Low Complexity Speech Enhancement Framework for Full-Band Audio based on Deep Filtering](https://mlsanity.com/paper/2110.05588/) (2021) - [GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks](https://mlsanity.com/paper/2210.13768/) (2022) - [TinyLSTMs: Efficient Neural Speech Enhancement for Hearing Aids](https://mlsanity.com/paper/2005.11138/) (2020) - [Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks](https://mlsanity.com/paper/2007.01204/) (2020) - [CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancement](https://mlsanity.com/paper/2209.11112/) (2022) - [Fast-SNN: Fast Spiking Neural Network by Converting Quantized ANN](https://mlsanity.com/paper/2305.19868/) (2023) - [A consolidated view of loss functions for supervised deep learning-based speech enhancement](https://mlsanity.com/paper/2009.12286/) (2020) - [Benchmarking Keyword Spotting Efficiency on Neuromorphic Hardware](https://mlsanity.com/paper/1812.01739/) (2018) - [Real-time Denoising and Dereverberation with Tiny Recurrent U-Net](https://mlsanity.com/paper/2102.03207/) (2021) - [Deep Spiking Neural Networks for Large Vocabulary Automatic Speech Recognition](https://mlsanity.com/paper/1911.08373/) (2019) - [TC-LIF: A Two-Compartment Spiking Neuron Model for Long-Term Sequential Modelling](https://mlsanity.com/paper/2308.13250/) (2023) - [The Intel Neuromorphic DNS Challenge](https://mlsanity.com/paper/2303.09503/) (2023) - [Fast FullSubNet: Accelerate Full-band and Sub-band Fusion Model for Single-channel Speech Enhancement](https://mlsanity.com/paper/2212.09019/) (2022) - [A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks](https://mlsanity.com/paper/2305.16594/) (2023) - [Investigating Cross-Domain Losses for Speech Enhancement](https://mlsanity.com/paper/2010.10468/) (2020) - [A Spiking Network that Learns to Extract Spike Signatures from Speech Signals](https://mlsanity.com/paper/1606.00802/) (2016) ## Built on by (most cited first, 1 of 1) - [IML-Spikeformer: Input-aware Multi-Level Spiking Transformer for Speech Processing](https://mlsanity.com/paper/2507.07396/) (2025) ## Abstract (arXiv) Speech enhancement is critical for improving speech intelligibility and quality in various audio devices. In recent years, deep learning-based methods have significantly improved speech enhancement performance, but they often come with a high computational cost, which is prohibitive for a large number of edge devices, such as headsets and hearing aids. This work proposes an ultra-low-power speech enhancement system based on the brain-inspired spiking neural network (SNN) called Spiking-FullSubNet. Spiking-FullSubNet follows a full-band and sub-band fusioned approach to effectively capture both global and local spectral information. To enhance the efficiency of computationally expensive sub-band modeling, we introduce a frequency partitioning method inspired by the sensitivity profile of the human peripheral auditory system. Furthermore, we introduce a novel spiking neuron model that can dynamically control the input information integration and forgetting, enhancing the multi-scale temporal processing capability of SNN, which is critical for speech denoising. Experiments conducted on the recent Intel Neuromorphic Deep Noise Suppression (N-DNS) Challenge dataset show that the Spiking-FullSubNet surpasses state-of-the-art methods by large margins in terms of both speech quality and energy efficiency metrics. Notably, our system won the championship of the Intel N-DNS Challenge (Algorithmic Track), opening up a myriad of opportunities for ultra-low-power speech enhancement at the edge. Our source code and model checkpoints are publicly available at https://github.com/haoxiangsnr/spiking-fullsubnet.