# The PCG-AIID System for L3DAS22 Challenge: MIMO and MISO convolutional recurrent Network for Multi Channel Speech Enhancement and Speech Recognition

arXiv: https://arxiv.org/abs/2202.10017 · HTML: https://mlsanity.com/paper/2202.10017/

## Key facts

- Cited by 4 papers in the map (data through 19 Oct 2026).
- Placed in Speech enhancement and noise suppression, one of 11 directions in Speech and audio language models.
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 21 Feb 2022.

## Placed in

- Speech and audio language models / [Speech enhancement and noise suppression](https://mlsanity.com/speech/speech-enhancement-and-noise-suppression/)

## Builds on (most cited first, 6 of 6)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [FSD50K: An Open Dataset of Human-Labeled Sound Events](https://mlsanity.com/paper/2010.00475/) (2020)
- [Supervised Speech Separation Based on Deep Learning: An Overview](https://mlsanity.com/paper/1708.07524/) (2017)
- [ADL-MVDR: All deep learning MVDR beamformer for target speech separation](https://mlsanity.com/paper/2008.06994/) (2020)
- [Multi-Microphone Complex Spectral Mapping for Speech Dereverberation](https://mlsanity.com/paper/2003.01861/) (2020)
- [Inplace Gated Convolutional Recurrent Neural Network For Dual-channel Speech Enhancement](https://mlsanity.com/paper/2107.11968/) (2021)

## Built on by (most cited first, 4 of 4)

- [TF-GridNet: Integrating Full- and Sub-Band Modeling for Speech Separation](https://mlsanity.com/paper/2211.12433/) (2022)
- [DeFTAN-II: Efficient Multichannel Speech Enhancement with Subgroup Processing](https://mlsanity.com/paper/2308.15777/) (2023)
- [A General Unfolding Speech Enhancement Method Motivated by Taylor's Theorem](https://mlsanity.com/paper/2211.16764/) (2022)
- [Data-independent Beamforming for End-to-end Multichannel Multi-speaker ASR](https://mlsanity.com/paper/2509.10234/) (2025)

## Abstract (arXiv)

This paper described the PCG-AIID system for L3DAS22 challenge in Task 1: 3D speech enhancement in office reverberant environment. We proposed a two-stage framework to address multi-channel speech denoising and dereverberation. In the first stage, a multiple input and multiple output (MIMO) network is applied to remove background noise while maintaining the spatial characteristics of multi-channel signals. In the second stage, a multiple input and single output (MISO) network is applied to enhance the speech from desired direction and post-filtering. As a result, our system ranked 3rd place in ICASSP2022 L3DAS22 challenge and significantly outperforms the baseline system, while achieving 3.2% WER and 0.972 STOI on the blind test-set.