SepMamba: State-space models for speaker separation using Mamba

• URL: http://arxiv.org/abs/2410.20997v1
• Date: Mon, 28 Oct 2024 13:20:53 GMT
• Title: SepMamba: State-space models for speaker separation using Mamba
• Authors: Thor Højhus Avenstrup, Boldizsár Elek, István László Mádi, András Bence Schin, Morten Mørup, Bjørn Sand Jensen, Kenny Falkær Olsen,
• Abstract summary: We propose SepMamba, a U-Net-based architecture composed primarily of bidirectional Mamba layers. We find that our approach outperforms similarly-sized prominent models on the WSJ0 2-speaker dataset.
• Score: 2.840381306234341
• License:
• Abstract: Deep learning-based single-channel speaker separation has improved significantly in recent years largely due to the introduction of the transformer-based attention mechanism. However, these improvements come at the expense of intense computational demands, precluding their use in many practical applications. As a computationally efficient alternative with similar modeling capabilities, Mamba was recently introduced. We propose SepMamba, a U-Net-based architecture composed primarily of bidirectional Mamba layers. We find that our approach outperforms similarly-sized prominent models - including transformer-based models - on the WSJ0 2-speaker dataset while enjoying a significant reduction in computational cost, memory usage, and forward pass time. We additionally report strong results for causal variants of SepMamba. Our approach provides a computationally favorable alternative to transformer-based architectures for deep speech separation.

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