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Recently, multi-channel speech enhancement has drawn much interest due to theuse of spatial information to distinguish target speech from interferingsignal. To make full use of spatial information and neural network basedmasking estimation, we propose a multi-channel denoising neural network --Spatial DCCRN. Firstly, we extend S-DCCRN to multi-channel scenario, aiming atperforming cascaded sub-channel and full-channel processing strategy, which canmodel different channels separately. Moreover, instead of only adoptingmulti-channel spectrum or concatenating first-channel's magnitude and IPD asthe model's inputs, we apply an angle feature extraction module (AFE) toextract frame-level angle feature embeddings, which can help the model toapparently perceive spatial information. Finally, since the phenomenon ofresidual noise will be more serious when the noise and speech exist in the sametime frequency (TF) bin, we particularly design a masking and mapping filteringmethod to substitute the traditional filter-and-sum operation, with the purposeof cascading coarsely denoising, dereverberation and residual noisesuppression. The proposed model, Spatial-DCCRN, has surpassed EaBNet, FasNet aswell as several competitive models on the L3DAS22 Challenge dataset. Not onlythe 3D scenario, Spatial-DCCRN outperforms state-of-the-art (SOTA) modelMIMO-UNet by a large margin in multiple evaluation metrics on the multi-channelConferencingSpeech2021 Challenge dataset. Ablation studies also demonstrate theeffectiveness of different contributions.

spatial-dccrn: dccrn equipped with frame-level angle feature and hybrid filtering for multi-channel speech enhancement