  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.
