# Spiking Structured State Space Model for Monaural Speech Enhancement

arXiv: https://arxiv.org/abs/2309.03641 · HTML: https://mlsanity.com/paper/2309.03641/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Placed in Speech and audio SSMs, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 17 of its 27 references that are in the map.
- First posted on arXiv 7 Sep 2023.

## Placed in

- State-space sequence models (S4, Mamba) / [Speech and audio SSMs](https://mlsanity.com/ssm/speech-and-audio-ssms/)

## Builds on (most cited first, 17 of 17)

- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [Simplified State Space Layers for Sequence Modeling](https://mlsanity.com/paper/2208.04933/) (2022)
- [Long Range Arena: A Benchmark for Efficient Transformers](https://mlsanity.com/paper/2011.04006/) (2020)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [HiPPO: Recurrent Memory with Optimal Polynomial Projections](https://mlsanity.com/paper/2008.07669/) (2020)
- [Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks](https://mlsanity.com/paper/1706.02609/) (2017)
- [Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks](https://mlsanity.com/paper/2007.05785/) (2020)
- [Attention is All You Need in Speech Separation](https://mlsanity.com/paper/2010.13154/) (2020)
- [MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement](https://mlsanity.com/paper/2104.03538/) (2021)
- [FRCRN: Boosting Feature Representation using Frequency Recurrence for Monaural Speech Enhancement](https://mlsanity.com/paper/2206.07293/) (2022)
- [Glance and Gaze: A Collaborative Learning Framework for Single-channel Speech Enhancement](https://mlsanity.com/paper/2106.11789/) (2021)
- [Improved Speech Enhancement with the Wave-U-Net](https://mlsanity.com/paper/1811.11307/) (2018)
- [Perceptual Loss based Speech Denoising with an ensemble of Audio Pattern Recognition and Self-Supervised Models](https://mlsanity.com/paper/2010.11860/) (2020)
- [The Intel Neuromorphic DNS Challenge](https://mlsanity.com/paper/2303.09503/) (2023)
- [A Multi-dimensional Deep Structured State Space Approach to Speech Enhancement Using Small-footprint Models](https://mlsanity.com/paper/2306.00331/) (2023)
- [Monaural Speech Enhancement with Complex Convolutional Block Attention Module and Joint Time Frequency Losses](https://mlsanity.com/paper/2102.01993/) (2021)
- [Single Channel Speech Enhancement Using U-Net Spiking Neural Networks](https://mlsanity.com/paper/2307.14464/) (2023)

## Built on by (most cited first, 23 of 23)

- [An Investigation of Incorporating Mamba for Speech Enhancement](https://mlsanity.com/paper/2405.06573/) (2024)
- [State Space Model for New-Generation Network Alternative to Transformers: A Survey](https://mlsanity.com/paper/2404.09516/) (2024)
- [Learning Long Sequences in Spiking Neural Networks](https://mlsanity.com/paper/2401.00955/) (2023)
- [SpikingSSMs: Learning Long Sequences with Sparse and Parallel Spiking State Space Models](https://mlsanity.com/paper/2408.14909/) (2024)
- [SPikE-SSM: A Sparse, Precise, and Efficient Spiking State Space Model for Long Sequences Learning](https://mlsanity.com/paper/2410.17268/) (2024)
- [Universal Speech Enhancement with Regression and Generative Mamba](https://mlsanity.com/paper/2505.21198/) (2025)
- [Holographic Global Convolutional Networks for Long-Range Prediction Tasks in Malware Detection](https://mlsanity.com/paper/2403.17978/) (2024)
- [A Diagonal Structured State Space Model on Loihi 2 for Efficient Streaming Sequence Processing](https://mlsanity.com/paper/2409.15022/) (2024)
- [Zero-Shot Temporal Resolution Domain Adaptation for Spiking Neural Networks](https://mlsanity.com/paper/2411.04760/) (2024)
- [DPSNN: Spiking Neural Network for Low-Latency Streaming Speech Enhancement](https://mlsanity.com/paper/2408.07388/) (2024)
- [FACTS: A Factored State-Space Framework For World Modelling](https://mlsanity.com/paper/2410.20922/) (2024)
- [xLSTM-SENet: xLSTM for Single-Channel Speech Enhancement](https://mlsanity.com/paper/2501.06146/) (2025)
- [Scaling Up Resonate-and-Fire Networks for Fast Deep Learning](https://mlsanity.com/paper/2504.00719/) (2025)
- [Quantizing Small-Scale State-Space Models for Edge AI](https://mlsanity.com/paper/2506.12480/) (2025)
- [Mamba-CAD: State Space Model For 3D Computer-Aided Design Generative Modeling](https://mlsanity.com/paper/2603.00439/) (2026)
- [Technologies on Effectiveness and Efficiency: A Survey of State Spaces Models](https://mlsanity.com/paper/2503.11224/) (2025)
- [MambAttention: Mamba with Multi-Head Attention for Generalizable Single-Channel Speech Enhancement](https://mlsanity.com/paper/2507.00966/) (2025)
- [IML-Spikeformer: Input-aware Multi-Level Spiking Transformer for Speech Processing](https://mlsanity.com/paper/2507.07396/) (2025)
- [aTENNuate: Optimized Real-time Speech Enhancement with Deep SSMs on Raw Audio](https://mlsanity.com/paper/2409.03377/) (2024)
- [Delays in Spiking Neural Networks: A State Space Model Approach](https://mlsanity.com/paper/2512.01906/) (2025)
- [Scale-covariant spiking wavelets](https://mlsanity.com/paper/2602.02020/) (2026)
- [LongSpike: Fractional Order Spiking State Space Models for Efficient Long Sequence Learning](https://mlsanity.com/paper/2606.12895/) (2026)
- [Neuromorphic Speech Enhancement with Dual-Branch Spiking Neural Networks](https://mlsanity.com/paper/2606.23761/) (2026)

## Abstract (arXiv)

Speech enhancement seeks to extract clean speech from noisy signals. Traditional deep learning methods face two challenges: efficiently using information in long speech sequences and high computational costs. To address these, we introduce the Spiking Structured State Space Model (Spiking-S4). This approach merges the energy efficiency of Spiking Neural Networks (SNN) with the long-range sequence modeling capabilities of Structured State Space Models (S4), offering a compelling solution. Evaluation on the DNS Challenge and VoiceBank+Demand Datasets confirms that Spiking-S4 rivals existing Artificial Neural Network (ANN) methods but with fewer computational resources, as evidenced by reduced parameters and Floating Point Operations (FLOPs).