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• W3196940724 abstract "Standing upon the intersection of traditional beamformers and deep neural networks, we propose a causal neural beamformer paradigm called Embedding and Beamforming, and two core modules are devised accordingly, namely EM and BM. For EM, instead of estimating spatial covariance matrix explicitly, the 3-D embedding tensor is learned with the network, where the spatial-spectral discriminative information can be implicitly represented. For BM, a network is directly leveraged to derive the beamforming weights so as to implement filter-and-sum operation. To further improve the speech quality, a post-processing module is introduced to further suppress the residual noise. Based on the DNS-Challenge dataset, we conduct the experiments for multichannel speech enhancement and the results show that the proposed system outperforms previous advanced baselines by a large margin in terms of multiple evaluation metrics." @default.
• W3196940724 created "2021-09-13" @default.
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• W3196940724 date "2022-05-23" @default.
• W3196940724 modified "2023-09-23" @default.
• W3196940724 title "Embedding and Beamforming: All-Neural Causal Beamformer for Multichannel Speech Enhancement" @default.
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• W3196940724 doi "https://doi.org/10.1109/icassp43922.2022.9746432" @default.
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