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Embedding and Beamforming: All-neural Causal Beamformer for Multichannel Speech Enhancement
Sound · Submitted 1 Sep 2021
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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 multiple evaluation metrics.
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The abstract, sentence by sentence
The spatial covariance matrix has been considered to be significant for beamformers. 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 designed 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 both spectral and spatial discriminative information can be 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 multiple evaluation metrics.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | The spatial covariance matrix has been considered to be significant for beamformers. | background 1.00 | 0.05 | 0.02 | asserted 0.99 |
| s1 | 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 designed accordingly, namely EM and BM. | contribution 0.99 | 0.91 | 0.07 | asserted 0.98 |
| s2 | For EM, instead of estimating spatial covariance matrix explicitly, the 3-D embedding tensor is learned with the network, where both spectral and spatial discriminative information can be represented. | method 0.97 | 0.86 | 0.04 | asserted 0.99 |
| s3 | For BM, a network is directly leveraged to derive the beamforming weights so as to implement filter-and-sum operation. | method 0.94 | 0.88 | 0.03 | asserted 0.98 |
| s4 | To further improve the speech quality, a post-processing module is introduced to further suppress the residual noise. | method 0.85 | 0.87 | 0.03 | asserted 0.89 |
| s5 | 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 multiple evaluation metrics. | result 1.00 | 0.97 | 0.88 | asserted 1.00 |
What was examined, and what was not
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The paper
Original abstract
The spatial covariance matrix has been considered to be significant for beamformers. 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 designed 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 both spectral and spatial discriminative information can be 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 multiple evaluation metrics.
Human review
How the literature uses it
2 arXiv papers cite this in 10 sentences
Papers citing it · 6 sentences without a confident label
Uses its method or tool 1 paper
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In this study, we adopt the similar network structure of our previous work [ref] .
TaylorBeamformer: Learning All-Neural Beamformer for Multi-Channel Speech Enhancement from Taylor's Approximation Theory · Network structure · 0.96
Compares with it 2 papers
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After combining the contributions above, Spatial-DCCRN has surpassed several SOTA models, including FasNet [ref] and EaBNet [ref] , and obtains 0.956 metric score which are composed of STOI and WER and ranks forth on the L3DAS22 challenge dataset.
spatial-dccrn: dccrn equipped with frame-level angle feature and hybrid filtering for multi-channel speech enhancement · Introduction · 1.00
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For causal case, three baselines are chosen, namely MMUB [ref] , EaBNet [ref] , and frame-wise MVDR beamformer with oracle speech and noise to calculate the covariance matrices [ref] .
TaylorBeamformer: Learning All-Neural Beamformer for Multi-Channel Speech Enhancement from Taylor's Approximation Theory · Configurations · 0.99
Cites it as background 1 paper
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More recently, all-neural frame-level beamformers are investigated in either time domain [ref] or time-frequency (T-F) domain [ref] .
TaylorBeamformer: Learning All-Neural Beamformer for Multi-Channel Speech Enhancement from Taylor's Approximation Theory · Introduction · 0.94
What it cites · 1 sentence without a confident label
Uses the method or tool of 3 papers
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Due to the space limit, we may refer the readers to [ref] for more details.
Glance and Gaze: A Collaborative Learning Framework for Single-channel Speech Enhancement · Post-processing Module · 0.87
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All the models are trained with Adam optimizer [ref] and the learning rate is initialized at 5e-4 and will be halved if the loss does not decrease for consecutive two epochs.
Adam: A Method for Stochastic Optimization · Training Details · 1.00
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For the second type, the layer norm (LN) [ref] is first adopted to normalize the embedding tensor, followed by two LSTM layers to simulate the beamforming process updated frame by frame, as shown in Fig. [ref](e).
Layer Normalization · Beamforming Module · 0.98
Compares with 1 paper
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Six approaches are selected as the baselines, namely CTSNet [ref] , GaGNet [ref] , FasNet+TAC [ref] , MC-ConvTasNet [ref] , MIMO-UNet [ref] , and oracle MB-MVDR.
Glance and Gaze: A Collaborative Learning Framework for Single-channel Speech Enhancement · Baselines · 0.98
Cites as background 1 paper
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Most recently, the efficacy of power-compression is investigated for single-channel speech enhancement [ref] and dereverberation tasks [ref] .
Glance and Gaze: A Collaborative Learning Framework for Single-channel Speech Enhancement · Training Details · 0.93
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