The PCG-AIID System for L3DAS22 Challenge: MIMO and MISO convolutional recurrent Network for Multi Channel Speech Enhancement and Speech Recognition
On the map: Speech and audio language models → Speech enhancement and noise suppression
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.
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.
Builds on
6 of 18 references.
References · 6
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 4,385
- 2020FSD50K: An Open Dataset of Human-Labeled Sound Eventscited by 395
- 2017Supervised Speech Separation Based on Deep Learning: An Overviewcited by 342
- 2020ADL-MVDR: All deep learning MVDR beamformer for target speech separationcited by 40
- 2020Multi-Microphone Complex Spectral Mapping for Speech Dereverberationcited by 25
- 2021Inplace Gated Convolutional Recurrent Neural Network For Dual-channel Speech Enhancementcited by 5
Built on by
Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 1.
4 papers.
- 2022TF-GridNet: Integrating Full- and Sub-Band Modeling for Speech Separationcited by 121
- 2023DeFTAN-II: Efficient Multichannel Speech Enhancement with Subgroup Processingcited by 7
- 2022A General Unfolding Speech Enhancement Method Motivated by Taylor's Theoremcited by 1
- 2025Data-independent Beamforming for End-to-end Multichannel Multi-speaker ASRcited by 0
- 2025Data-independent Beamforming for End-to-end Multichannel Multi-speaker ASR12 Sep 2025
- 2023DeFTAN-II: Efficient Multichannel Speech Enhancement with Subgroup Processing30 Aug 2023
- 2022A General Unfolding Speech Enhancement Method Motivated by Taylor's Theorem30 Nov 2022
- 2022TF-GridNet: Integrating Full- and Sub-Band Modeling for Speech Separation22 Nov 2022
Similar, not cited
Close in wording, no citation either way
- 2022Towards Low-distortion Multi-channel Speech Enhancement: The ESPNet-SE Submission to The L3DAS22 Challenge
- 2022Insights Into Deep Non-linear Filters for Improved Multi-channel Speech Enhancement
- 2021Multichannel Speech Enhancement without Beamforming
- 2022On the Role of Spatial, Spectral, and Temporal Processing for DNN-based Non-linear Multi-channel Speech Enhancement
- 2022spatial-dccrn: dccrn equipped with frame-level angle feature and hybrid filtering for multi-channel speech enhancement
- 2021Towards efficient models for real-time deep noise suppression
- 2022Speech Enhancement with Intelligent Neural Homomorphic Synthesis
- 2025A Lightweight Hybrid Dual Channel Speech Enhancement System under Low-SNR Conditions