# Omni-directional attention mechanism based on Mamba for speech separation

arXiv: https://arxiv.org/abs/2601.16603 · HTML: https://mlsanity.com/paper/2601.16603/

## Key facts

- Cited by 1 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 30 of its 35 references that are in the map.
- First posted on arXiv 23 Jan 2026.

## 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, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024)
- [Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers](https://mlsanity.com/paper/2110.13985/) (2021)
- [On the Parameterization and Initialization of Diagonal State Space Models](https://mlsanity.com/paper/2206.11893/) (2022)
- [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)
- [Supervised Speech Separation Based on Deep Learning: An Overview](https://mlsanity.com/paper/1708.07524/) (2017)
- [Deep clustering: Discriminative embeddings for segmentation and separation](https://mlsanity.com/paper/1508.04306/) (2015)
- [Attention is All You Need in Speech Separation](https://mlsanity.com/paper/2010.13154/) (2020)
- [Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation](https://mlsanity.com/paper/1910.06379/) (2019)
- [WHAM!: Extending Speech Separation to Noisy Environments](https://mlsanity.com/paper/1907.01160/) (2019)
- [LibriMix: An Open-Source Dataset for Generalizable Speech Separation](https://mlsanity.com/paper/2005.11262/) (2020)
- [VmambaIR: Visual State Space Model for Image Restoration](https://mlsanity.com/paper/2403.11423/) (2024)
- [Dual-Path Transformer Network: Direct Context-Aware Modeling for End-to-End Monaural Speech Separation](https://mlsanity.com/paper/2007.13975/) (2020)
- [MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts](https://mlsanity.com/paper/2401.04081/) (2024)
- [Sudo rm -rf: Efficient Networks for Universal Audio Source Separation](https://mlsanity.com/paper/2007.06833/) (2020)
- [TF-GridNet: Making Time-Frequency Domain Models Great Again for Monaural Speaker Separation](https://mlsanity.com/paper/2209.03952/) (2022)
- [Wave-Mamba: Wavelet State Space Model for Ultra-High-Definition Low-Light Image Enhancement](https://mlsanity.com/paper/2408.01276/) (2024)
- [Dual-path Mamba: Short and Long-term Bidirectional Selective Structured State Space Models for Speech Separation](https://mlsanity.com/paper/2403.18257/) (2024)
- [An efficient encoder-decoder architecture with top-down attention for speech separation](https://mlsanity.com/paper/2209.15200/) (2022)
- [Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network](https://mlsanity.com/paper/2112.02321/) (2021)
- [ClinicalMamba: A Generative Clinical Language Model on Longitudinal Clinical Notes](https://mlsanity.com/paper/2403.05795/) (2024)
- [Exploring the Capability of Mamba in Speech Applications](https://mlsanity.com/paper/2406.16808/) (2024)
- [On the Use of Deep Mask Estimation Module for Neural Source Separation Systems](https://mlsanity.com/paper/2206.07347/) (2022)
- [Mamba-SEUNet: Mamba UNet for Monaural Speech Enhancement](https://mlsanity.com/paper/2412.16626/) (2024)
- [A Neural State-Space Model Approach to Efficient Speech Separation](https://mlsanity.com/paper/2305.16932/) (2023)
- [SepMamba: State-space models for speaker separation using Mamba](https://mlsanity.com/paper/2410.20997/) (2024)
- [U-Mamba-Net: A highly efficient Mamba-based U-net style network for noisy and reverberant speech separation](https://mlsanity.com/paper/2412.18217/) (2024)

## Built on by (most cited first, 1 of 1)

- [Dual-View Predictive Diffusion: Lightweight Speech Enhancement via Spectrogram-Image Synergy](https://mlsanity.com/paper/2602.00568/) (2026)

## Abstract (arXiv)

Mamba, a selective state-space model (SSM), has emerged as an efficient alternative to Transformers for speech modeling, enabling long-sequence processing with linear complexity. While effective in speech separation, existing approaches, whether in the time or time-frequency domain, typically decompose the input along a single dimension into short one-dimensional sequences before processing them with Mamba, which restricts it to local 1D modeling and limits its ability to capture global dependencies across the 2D spectrogram. In this work, we propose an efficient omni-directional attention (OA) mechanism built upon unidirectional Mamba, which models global dependencies from ten different directions on the spectrogram. We expand the proposed mechanism into two baseline separation models and evaluate on three public datasets. Experimental results show that our approach consistently achieves significant performance gains over the baselines while preserving linear complexity, outperforming existing state-of-the-art (SOTA) systems.