CrossNet: Leveraging Global, Cross-Band, Narrow-Band, and Positional Encoding for Single- and Multi-Channel Speaker Separation
<|reference_start|>CrossNet: Leveraging Global, Cross-Band, Narrow-Band, and Positional Encoding for Single- and Multi-Channel Speaker Separation: We introduce CrossNet, a complex spectral mapping approach to speaker separation and enhancement in reverberant and noisy conditions. The proposed architecture comprises an encoder layer, a global multi-head self-attention module, a cross-band module, a narrow-band module, and an output layer. CrossNet captures global, cross-band, and narrow-band correlations in the time-frequency domain. To address performance degradation in long utterances, we introduce a random chunk positional encoding. Experimental results on multiple datasets demonstrate the effectiveness and robustness of CrossNet, achieving state-of-the-art performance in tasks including reverberant and noisy-reverberant speaker separation. Furthermore, CrossNet exhibits faster and more stable training in comparison to recent baselines. Additionally, CrossNet's high performance extends to multi-microphone conditions, demonstrating its versatility in various acoustic scenarios.<|reference_end|>
@article{kalkhorani2024crossnet:,
    title={CrossNet: Leveraging Global, Cross-Band, Narrow-Band, and Positional
  Encoding for Single- and Multi-Channel Speaker Separation},
    author={Vahid Ahmadi Kalkhorani, DeLiang Wang},
    journal={arXiv preprint arXiv:2403.03411},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.03411},
    primaryClass={cs.SD eess.AS}
}