# Channel-Attention Dense U-Net for Multichannel Speech Enhancement

arXiv: https://arxiv.org/abs/2001.11542 · HTML: https://mlsanity.com/paper/2001.11542/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2020.

## Built on by (most cited first, 19 of 19)

- [End-to-End Multi-Channel Transformer for Speech Recognition](https://mlsanity.com/paper/2102.03951/) (2021)
- [Insights Into Deep Non-linear Filters for Improved Multi-channel Speech Enhancement](https://mlsanity.com/paper/2206.13310/) (2022)
- [Multi-channel Speech Separation Using Spatially Selective Deep Non-linear Filters](https://mlsanity.com/paper/2304.12023/) (2023)
- [McNet: Fuse Multiple Cues for Multichannel Speech Enhancement](https://mlsanity.com/paper/2211.08872/) (2022)
- [Uncertainty-Guided Lung Nodule Segmentation with Feature-Aware Attention](https://mlsanity.com/paper/2110.12372/) (2021)
- [On the Role of Spatial, Spectral, and Temporal Processing for DNN-based Non-linear Multi-channel Speech Enhancement](https://mlsanity.com/paper/2206.11181/) (2022)
- [Implicit Neural Spatial Filtering for Multichannel Source Separation in the Waveform Domain](https://mlsanity.com/paper/2206.15423/) (2022)
- [MIMO Self-attentive RNN Beamformer for Multi-speaker Speech Separation](https://mlsanity.com/paper/2104.08450/) (2021)
- [Self-Attention Channel Combinator Frontend for End-to-End Multichannel Far-field Speech Recognition](https://mlsanity.com/paper/2109.04783/) (2021)
- [Spatially Selective Deep Non-linear Filters for Speaker Extraction](https://mlsanity.com/paper/2211.02420/) (2022)
- [Deep Neural Mel-Subband Beamformer for In-car Speech Separation](https://mlsanity.com/paper/2211.12590/) (2022)
- [A Training Framework for Stereo-Aware Speech Enhancement using Deep Neural Networks](https://mlsanity.com/paper/2112.04939/) (2021)
- [MFF-EINV2: Multi-scale Feature Fusion across Spectral-Spatial-Temporal Domains for Sound Event Localization and Detection](https://mlsanity.com/paper/2406.08771/) (2024)
- [Leveraging Joint Spectral and Spatial Learning with MAMBA for Multichannel Speech Enhancement](https://mlsanity.com/paper/2409.10376/) (2024)
- [Challenges and Opportunities in Multi-device Speech Processing](https://mlsanity.com/paper/2206.15432/) (2022)
- [Injecting Spatial Information for Monaural Speech Enhancement via Knowledge Distillation](https://mlsanity.com/paper/2212.01012/) (2022)
- [Attention-Driven Multichannel Speech Enhancement in Moving Sound Source Scenarios](https://mlsanity.com/paper/2312.10756/) (2023)
- [RelUNet: Relative Channel Fusion U-Net for Multichannel Speech Enhancement](https://mlsanity.com/paper/2410.05019/) (2024)
- [AMDM-SE: Attention-based Multichannel Diffusion Model for Speech Enhancement](https://mlsanity.com/paper/2601.13140/) (2026)

## Abstract (arXiv)

Supervised deep learning has gained significant attention for speech enhancement recently. The state-of-the-art deep learning methods perform the task by learning a ratio/binary mask that is applied to the mixture in the time-frequency domain to produce the clean speech. Despite the great performance in the single-channel setting, these frameworks lag in performance in the multichannel setting as the majority of these methods a) fail to exploit the available spatial information fully, and b) still treat the deep architecture as a black box which may not be well-suited for multichannel audio processing. This paper addresses these drawbacks, a) by utilizing complex ratio masking instead of masking on the magnitude of the spectrogram, and more importantly, b) by introducing a channel-attention mechanism inside the deep architecture to mimic beamforming. We propose Channel-Attention Dense U-Net, in which we apply the channel-attention unit recursively on feature maps at every layer of the network, enabling the network to perform non-linear beamforming. We demonstrate the superior performance of the network against the state-of-the-art approaches on the CHiME-3 dataset.