# Embedding and Beamforming: All-neural Causal Beamformer for Multichannel Speech Enhancement

arXiv: https://arxiv.org/abs/2109.00265 · HTML: https://mlsanity.com/paper/2109.00265/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2021.

## Built on by (most cited first, 11 of 11)

- [Insights Into Deep Non-linear Filters for Improved Multi-channel Speech Enhancement](https://mlsanity.com/paper/2206.13310/) (2022)
- [Spatially Selective Deep Non-linear Filters for Speaker Extraction](https://mlsanity.com/paper/2211.02420/) (2022)
- [Guided Speech Enhancement Network](https://mlsanity.com/paper/2303.07486/) (2023)
- [Binaural Angular Separation Network](https://mlsanity.com/paper/2401.08864/) (2024)
- [Enhanced Neural Beamformer with Spatial Information for Target Speech Extraction](https://mlsanity.com/paper/2306.15942/) (2023)
- [End-to-end multi-channel speaker extraction and binaural speech synthesis](https://mlsanity.com/paper/2410.05739/) (2024)
- [End-to-End Multi-Microphone Speaker Extraction Using Relative Transfer Functions](https://mlsanity.com/paper/2502.06285/) (2025)
- [Leveraging Local and Global Knowledge Integration with Time-Frequency Calibrated Distillation for Speech Enhancement](https://mlsanity.com/paper/2506.13127/) (2025)
- [CabinSep: IR-Augmented Mask-Based MVDR for Real-Time In-Car Speech Separation with Distributed Heterogeneous Arrays](https://mlsanity.com/paper/2509.01399/) (2025)
- [CaSNet: Compress-and-Send Network Based Multi-Device Speech Enhancement Model for Distributed Microphone Arrays](https://mlsanity.com/paper/2601.17711/) (2026)
- [Transformer-based Neural Beamforming for Real-Time Speech Enhancement on Smart Low-Power Hearable Devices](https://mlsanity.com/paper/2609.33755/) (2026)

## Abstract (arXiv)

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.