# McNet: Fuse Multiple Cues for Multichannel Speech Enhancement

arXiv: https://arxiv.org/abs/2211.08872 · HTML: https://mlsanity.com/paper/2211.08872/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 16 Nov 2022.

## Builds on (most cited first, 10 of 10)

- [Supervised Speech Separation Based on Deep Learning: An Overview](https://mlsanity.com/paper/1708.07524/) (2017)
- [DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement](https://mlsanity.com/paper/2008.00264/) (2020)
- [FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement](https://mlsanity.com/paper/2010.15508/) (2020)
- [Glance and Gaze: A Collaborative Learning Framework for Single-channel Speech Enhancement](https://mlsanity.com/paper/2106.11789/) (2021)
- [Weighted Speech Distortion Losses for Neural-network-based Real-time Speech Enhancement](https://mlsanity.com/paper/2001.10601/) (2020)
- [Channel-Attention Dense U-Net for Multichannel Speech Enhancement](https://mlsanity.com/paper/2001.11542/) (2020)
- [Insights Into Deep Non-linear Filters for Improved Multi-channel Speech Enhancement](https://mlsanity.com/paper/2206.13310/) (2022)
- [Narrow-band Deep Filtering for Multichannel Speech Enhancement](https://mlsanity.com/paper/1911.10791/) (2019)
- [Online Monaural Speech Enhancement Using Delayed Subband LSTM](https://mlsanity.com/paper/2005.05037/) (2020)
- [Unsupervised Speech Enhancement Based on Multichannel NMF-Informed Beamforming for Noise-Robust Automatic Speech Recognition](https://mlsanity.com/paper/1903.09341/) (2019)

## Built on by (most cited first, 9 of 9)

- [Multi-channel Speech Separation Using Spatially Selective Deep Non-linear Filters](https://mlsanity.com/paper/2304.12023/) (2023)
- [LMFCA-Net: A Lightweight Model for Multi-Channel Speech Enhancement with Efficient Narrow-Band and Cross-Band Attention](https://mlsanity.com/paper/2502.11462/) (2025)
- [Adaptive Rotary Steering with Joint Autoregression for Robust Extraction of Closely Moving Speakers in Dynamic Scenarios](https://mlsanity.com/paper/2601.12345/) (2026)
- [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)
- [Mask-Weighted Spatial Likelihood Coding for Speaker-Independent Joint Localization and Mask Estimation](https://mlsanity.com/paper/2410.19595/) (2024)
- [AMDM-SE: Attention-based Multichannel Diffusion Model for Speech Enhancement](https://mlsanity.com/paper/2601.13140/) (2026)
- [CaSNet: Compress-and-Send Network Based Multi-Device Speech Enhancement Model for Distributed Microphone Arrays](https://mlsanity.com/paper/2601.17711/) (2026)
- [Deep Learning Based Relative Transfer Matrix Estimation for Multiple Sources and Multiple Microphones](https://mlsanity.com/paper/2608.11627/) (2026)

## Abstract (arXiv)

In multichannel speech enhancement, both spectral and spatial information are vital for discriminating between speech and noise. How to fully exploit these two types of information and their temporal dynamics remains an interesting research problem. As a solution to this problem, this paper proposes a multi-cue fusion network named McNet, which cascades four modules to respectively exploit the full-band spatial, narrow-band spatial, sub-band spectral, and full-band spectral information. Experiments show that each module in the proposed network has its unique contribution and, as a whole, notably outperforms other state-of-the-art methods.