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arXiv · 2308.15990

Dual-path Transformer Based Neural Beamformer for Target Speech Extraction

Aoqi Guo·Sichong Qian·Baoxiang Li·Dazhi Gao
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Abstract

Neural beamformers, which integrate both pre-separation and beamforming modules, have demonstrated impressive effectiveness in target speech extraction. Nevertheless, the performance of these beamformers is inherently limited by the predictive accuracy of the pre-separation module. In this paper, we introduce a neural beamformer supported by a dual-path transformer. Initially, we employ the cross-attention mechanism in the time domain to extract crucial spatial information related to beamforming from the noisy covariance matrix. Subsequently, in the frequency domain, the self-attention mechanism is employed to enhance the model's ability to process frequency-specific details. By design, our model circumvents the influence of pre-separation modules, delivering performance in a more comprehensive end-to-end manner. Experimental results reveal that our model not only outperforms contemporary leading neural beamforming algorithms in separation performance but also achieves this with a significant reduction in parameter count.

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Aoqi Guo, Sichong Qian, Baoxiang Li, Dazhi Gao. 2023-08-30. Dual-path Transformer Based Neural Beamformer for Target Speech Extraction. https://arxiv.org/abs/2308.15990

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