# Multi-Microphone Complex Spectral Mapping for Speech Dereverberation

arXiv: https://arxiv.org/abs/2003.01861 · HTML: https://mlsanity.com/paper/2003.01861/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2020.

## Built on by (most cited first, 8 of 8)

- [Implicit Neural Spatial Filtering for Multichannel Source Separation in the Waveform Domain](https://mlsanity.com/paper/2206.15423/) (2022)
- [Audio-visual End-to-end Multi-channel Speech Separation, Dereverberation and Recognition](https://mlsanity.com/paper/2307.02909/) (2023)
- [Multi-Channel End-to-End Neural Diarization with Distributed Microphones](https://mlsanity.com/paper/2110.04694/) (2021)
- [Audio-visual multi-channel speech separation, dereverberation and recognition](https://mlsanity.com/paper/2204.01977/) (2022)
- [A Survey of Deep Learning for Complex Speech Spectrograms](https://mlsanity.com/paper/2505.08694/) (2025)
- [Multichannel-to-Multichannel Target Sound Extraction Using Direction and Timestamp Clues](https://mlsanity.com/paper/2409.12415/) (2024)
- [Unsupervised Multi-channel Speech Dereverberation via Diffusion](https://mlsanity.com/paper/2508.02071/) (2025)
- [Cloud-Boosted Low-Compute Multi-Channel Speech Enhancement](https://mlsanity.com/paper/2608.07423/) (2026)

## Abstract (arXiv)

This study proposes a multi-microphone complex spectral mapping approach for speech dereverberation on a fixed array geometry. In the proposed approach, a deep neural network (DNN) is trained to predict the real and imaginary (RI) components of direct sound from the stacked reverberant (and noisy) RI components of multiple microphones. We also investigate the integration of multi-microphone complex spectral mapping with beamforming and post-filtering. Experimental results on multi-channel speech dereverberation demonstrate the effectiveness of the proposed approach.