# Deep Active Speech Cancellation with Mamba-Masking Network

arXiv: https://arxiv.org/abs/2502.01185 · HTML: https://mlsanity.com/paper/2502.01185/

## Key facts

- Cited by 2 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 22 of its 76 references that are in the map.
- First posted on arXiv 3 Feb 2025.

## 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, 22 of 22)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://mlsanity.com/paper/2405.21060/) (2024)
- [An Investigation of Incorporating Mamba for Speech Enhancement](https://mlsanity.com/paper/2405.06573/) (2024)
- [Mamba in Speech: Towards an Alternative to Self-Attention](https://mlsanity.com/paper/2405.12609/) (2024)
- [Dual-path Mamba: Short and Long-term Bidirectional Selective Structured State Space Models for Speech Separation](https://mlsanity.com/paper/2403.18257/) (2024)
- [SSAMBA: Self-Supervised Audio Representation Learning with Mamba State Space Model](https://mlsanity.com/paper/2405.11831/) (2024)
- [Audio Mamba: Bidirectional State Space Model for Audio Representation Learning](https://mlsanity.com/paper/2406.03344/) (2024)
- [Multichannel Long-Term Streaming Neural Speech Enhancement for Static and Moving Speakers](https://mlsanity.com/paper/2403.07675/) (2024)
- [RawBMamba: End-to-End Bidirectional State Space Model for Audio Deepfake Detection](https://mlsanity.com/paper/2406.06086/) (2024)
- [Speech Slytherin: Examining the Performance and Efficiency of Mamba for Speech Separation, Recognition, and Synthesis](https://mlsanity.com/paper/2407.09732/) (2024)
- [Audio Mamba: Selective State Spaces for Self-Supervised Audio Representations](https://mlsanity.com/paper/2406.02178/) (2024)
- [Rethinking Mamba in Speech Processing by Self-Supervised Models](https://mlsanity.com/paper/2409.07273/) (2024)
- [Audio Mamba: Pretrained Audio State Space Model For Audio Tagging](https://mlsanity.com/paper/2405.13636/) (2024)
- [Wave-U-Mamba: An End-To-End Framework For High-Quality And Efficient Speech Super Resolution](https://mlsanity.com/paper/2409.09337/) (2024)
- [Self-attending RNN for Speech Enhancement to Improve Cross-corpus Generalization](https://mlsanity.com/paper/2105.12831/) (2021)
- [A Hybrid SFANC-FxNLMS Algorithm for Active Noise Control based on Deep Learning](https://mlsanity.com/paper/2208.08082/) (2022)
- [SELD-Mamba: Selective State-Space Model for Sound Event Localization and Detection with Source Distance Estimation](https://mlsanity.com/paper/2408.05057/) (2024)
- [Deep Generative Fixed-filter Active Noise Control](https://mlsanity.com/paper/2303.05788/) (2023)
- [TF-Mamba: A Time-Frequency Network for Sound Source Localization](https://mlsanity.com/paper/2409.05034/) (2024)
- [Spatially Selective Active Noise Control Systems](https://mlsanity.com/paper/2208.09997/) (2022)
- [Unsupervised learning based end-to-end delayless generative fixed-filter active noise control](https://mlsanity.com/paper/2402.09460/) (2024)

## Built on by (most cited first, 2 of 2)

- [Active Speech Enhancement: Active Speech Denoising Decliping and Deveraberation](https://mlsanity.com/paper/2505.16911/) (2025)
- [Toward Optimal ANC: Establishing Mutual Information Lower Bound](https://mlsanity.com/paper/2505.17877/) (2025)

## Abstract (arXiv)

We present a novel deep learning network for Active Speech Cancellation (ASC), advancing beyond Active Noise Cancellation (ANC) methods by effectively canceling both noise and speech signals. The proposed Mamba-Masking architecture introduces a masking mechanism that directly interacts with the encoded reference signal, enabling adaptive and precisely aligned anti-signal generation-even under rapidly changing, high-frequency conditions, as commonly found in speech. Complementing this, a multi-band segmentation strategy further improves phase alignment across frequency bands. Additionally, we introduce an optimization-driven loss function that provides near-optimal supervisory signals for anti-signal generation. Experimental results demonstrate substantial performance gains, achieving up to 7.2dB improvement in ANC scenarios and 6.2dB in ASC, significantly outperforming existing methods.