MC-SEMamba: A Simple Multi-channel Extension of SEMamba
<|reference_start|>MC-SEMamba: A Simple Multi-channel Extension of SEMamba: Transformer-based models have become increasingly popular and have impacted speech-processing research owing to their exceptional performance in sequence modeling. Recently, a promising model architecture, Mamba, has emerged as a potential alternative to transformer-based models because of its efficient modeling of long sequences. In particular, models like SEMamba have demonstrated the effectiveness of the Mamba architecture in single-channel speech enhancement. This paper aims to adapt SEMamba for multi-channel applications with only a small increase in parameters. The resulting system, MC-SEMamba, achieved results on the CHiME3 dataset that were comparable or even superior to several previous baseline models. Additionally, we found that increasing the number of microphones from 1 to 6 improved the speech enhancement performance of MC-SEMamba.<|reference_end|>
@article{ting2024mc-semamba:,
    title={MC-SEMamba: A Simple Multi-channel Extension of SEMamba},
    author={Wen-Yuan Ting, Wenze Ren, Rong Chao, Hsin-Yi Lin, Yu Tsao, Fan-Gang
  Zeng},
    journal={arXiv preprint arXiv:2409.17898},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.17898},
    primaryClass={eess.AS cs.SD}
}