Spatial-DCCRN: DCCRN Equipped with Frame-Level Angle Feature and Hybrid Filtering for Multi-Channel Speech Enhancement

Shubo Lv, Yihui Fu, Yukai Jv, Lei Xie, Weixin Zhu, Wei Rao, Yannan Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

Recently, multi-channel speech enhancement has drawn much interest due to the use of spatial information to distinguish target speech from interfering signal. To make full use of spatial information and neural network based masking estimation, we propose a multi-channel denoising neural network - Spatial DCCRN. Firstly, we extend S-DCCRN to multi -channel scenario, aiming at performing cascaded sub-channel and full-channel processing strategy, which can model different channels separately. Moreover, instead of only adopting multi-channel spectrum or concatenating first-channel's magnitude and IPD as the model's inputs, we apply an angle feature extraction module (AFE) to extract frame-level angle feature embeddings, which can help the model to apparently perceive spatial information. Finally, since the phenomenon of residual noise will be more serious when the noise and speech exist in the same time frequency (TF) bin, we particularly design a masking and mapping filtering method to substitute the traditional filter-and-sum operation, with the purpose of cascading coarsely denoising, dereverberation and residual noise suppression. The proposed model, Spatial-DCCRN, has surpassed EaBNet, FasNet as well as several competitive models on the L3DAS22 Challenge dataset. Not only the 3D scenario, Spatial-DCCRN outperforms state-of-the-art (SOTA) model MIMO-UNet by a large margin in multiple evaluation metrics on the multi-channel ConferencingSpeech2021 Challenge dataset. Ablation studies also demonstrate the effectiveness of different contributions.

Original languageEnglish
Title of host publication2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages436-443
Number of pages8
ISBN (Electronic)9798350396904
DOIs
StatePublished - 2023
Event2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Doha, Qatar
Duration: 9 Jan 202312 Jan 2023

Publication series

Name2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings

Conference

Conference2022 IEEE Spoken Language Technology Workshop, SLT 2022
Country/TerritoryQatar
CityDoha
Period9/01/2312/01/23

Keywords

  • multi-channel
  • Spatial-DCCRN
  • speech enhancement

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