mlsanityData to 7 Oct 2026

Spiking Structured State Space Model for Monaural Speech Enhancement

arXiv 2309.03641 ↗First posted 7 Sep 2023cs.SD cs.CV eess.AScited by 23

On the map: State-space sequence models (S4, Mamba) → Speech and audio SSMs

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).

Key facts. Cited by 23 papers in the map (data through 7 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.

Builds on

17 of 27 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023A Multi-dimensional Deep Structured State Space Approach to Speech Enhancement Using Small-footprint Models1 Jun

Other references · 16

  1. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,424
  2. 2022Simplified State Space Layers for Sequence Modelingcited by 776
  3. 2020Long Range Arena: A Benchmark for Efficient Transformerscited by 696
  4. 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 604
  5. 2020HiPPO: Recurrent Memory with Optimal Polynomial Projectionscited by 600
  6. 2017Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networkscited by 399
  7. 2020Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networkscited by 340
  8. 2020Attention is All You Need in Speech Separationcited by 184
  9. 2021MetricGAN+: An Improved Version of MetricGAN for Speech Enhancementcited by 114
  10. 2021Glance and Gaze: A Collaborative Learning Framework for Single-channel Speech Enhancementcited by 25
  11. 2022FRCRN: Boosting Feature Representation using Frequency Recurrence for Monaural Speech Enhancementcited by 24
  12. 2018Improved Speech Enhancement with the Wave-U-Netcited by 23
4 more
  1. 2020Perceptual Loss based Speech Denoising with an ensemble of Audio Pattern Recognition and Self-Supervised Modelscited by 10
  2. 2023The Intel Neuromorphic DNS Challengecited by 9
  3. 2021Monaural Speech Enhancement with Complex Convolutional Block Attention Module and Joint Time Frequency Lossescited by 5
  4. 2023Single Channel Speech Enhancement Using U-Net Spiking Neural Networkscited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 10; 2025: 8; 2026: 4.

23 papers.

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  1. 2024An Investigation of Incorporating Mamba for Speech Enhancementcited by 61
  2. 2024State Space Model for New-Generation Network Alternative to Transformers: A Surveycited by 59
  3. 2023Learning Long Sequences in Spiking Neural Networkscited by 19
  4. 2024SpikingSSMs: Learning Long Sequences with Sparse and Parallel Spiking State Space Modelscited by 19
  5. 2024SPikE-SSM: A Sparse, Precise, and Efficient Spiking State Space Model for Long Sequences Learningcited by 9
  6. 2025Universal Speech Enhancement with Regression and Generative Mambacited by 7
  7. 2024Holographic Global Convolutional Networks for Long-Range Prediction Tasks in Malware Detectioncited by 6
  8. 2024A Diagonal Structured State Space Model on Loihi 2 for Efficient Streaming Sequence Processingcited by 6
  9. 2024Zero-Shot Temporal Resolution Domain Adaptation for Spiking Neural Networkscited by 6
  10. 2024DPSNN: Spiking Neural Network for Low-Latency Streaming Speech Enhancementcited by 5
  11. 2024FACTS: A Factored State-Space Framework For World Modellingcited by 4
  12. 2025xLSTM-SENet: xLSTM for Single-Channel Speech Enhancementcited by 4
11 more
  1. 2025Scaling Up Resonate-and-Fire Networks for Fast Deep Learningcited by 4
  2. 2025Quantizing Small-Scale State-Space Models for Edge AIcited by 3
  3. 2026Mamba-CAD: State Space Model For 3D Computer-Aided Design Generative Modelingcited by 3
  4. 2025Technologies on Effectiveness and Efficiency: A Survey of State Spaces Modelscited by 2
  5. 2025MambAttention: Mamba with Multi-Head Attention for Generalizable Single-Channel Speech Enhancementcited by 2
  6. 2025IML-Spikeformer: Input-aware Multi-Level Spiking Transformer for Speech Processingcited by 1
  7. 2024aTENNuate: Optimized Real-time Speech Enhancement with Deep SSMs on Raw Audiocited by 0
  8. 2025Delays in Spiking Neural Networks: A State Space Model Approachcited by 0
  9. 2026Scale-covariant spiking waveletscited by 0
  10. 2026LongSpike: Fractional Order Spiking State Space Models for Efficient Long Sequence Learningcited by 0
  11. 2026Neuromorphic Speech Enhancement with Dual-Branch Spiking Neural Networkscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025SiLIF: Structured State Space Model Dynamics and Parametrization for Spiking Neural Networks
  2. 2024PRF: Parallel Resonate and Fire Neuron for Long Sequence Learning in Spiking Neural Networks
  3. 2024Modulating State Space Model with SlowFast Framework for Compute-Efficient Ultra Low-Latency Speech Enhancement
  4. 2026NeuronSpark: A Spiking Neural Network Language Model with Selective State Space Dynamics
  5. 2026DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement
  6. 2026Phase State Space Models: Parallel, Surrogate-Free Training of Spiking Networks ↗
  7. 2023A Neural State-Space Model Approach to Efficient Speech Separation
  8. 2026Spiking and Event-driven Neuromorphic Mamba Models for Efficient Speech Recognition