English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Neural beamformers, which integrate both pre-separation and beamformingmodules, have demonstrated impressive effectiveness in target speechextraction. Nevertheless, the performance of these beamformers is inherentlylimited 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 toextract crucial spatial information related to beamforming from the noisycovariance matrix. Subsequently, in the frequency domain, the self-attentionmechanism is employed to enhance the model's ability to processfrequency-specific details. By design, our model circumvents the influence ofpre-separation modules, delivering performance in a more comprehensiveend-to-end manner. Experimental results reveal that our model not onlyoutperforms contemporary leading neural beamforming algorithms in separationperformance but also achieves this with a significant reduction in parametercount.

Dual-path Transformer Based Neural Beamformer for Target Speech Extraction