Gespeichert in:
| Hauptverfasser: | , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2410.20997 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866917819363360768 |
|---|---|
| author | Avenstrup, Thor Højhus Elek, Boldizsár Mádi, István László Schin, András Bence Mørup, Morten Jensen, Bjørn Sand Olsen, Kenny Falkær |
| author_facet | Avenstrup, Thor Højhus Elek, Boldizsár Mádi, István László Schin, András Bence Mørup, Morten Jensen, Bjørn Sand Olsen, Kenny Falkær |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_20997 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SepMamba: State-space models for speaker separation using Mamba Avenstrup, Thor Højhus Elek, Boldizsár Mádi, István László Schin, András Bence Mørup, Morten Jensen, Bjørn Sand Olsen, Kenny Falkær Sound Machine Learning Audio and Speech Processing 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. |
| title | SepMamba: State-space models for speaker separation using Mamba |
| topic | Sound Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2410.20997 |