空间-dccrn：用于多通道语音增强配备有帧级角度特征和混合滤波的dccrn
spatial-dccrn: dccrn equipped with frame-level angle feature and hybrid filtering for multi-channel speech enhancement
摘要：
近年来，多通道语音增强由于能够利用空间信息区分目标语音和干扰信号而受到广泛关注。为了充分利用空间信息和基于神经网络的掩蔽估计，提出了一种多通道去噪神经网络--空间DCCRN。首先，我们将S-DCCRN扩展到多信道场景，旨在执行级联的子信道和全信道处理策略，可以单独建模不同的信道。此外，而不是仅仅采用多通道频谱或级联的第一通道的幅度和IPD作为模型的输入，我们应用一个角度特征提取模块（AFE）提取帧级的角度特征嵌入，这可以帮助模型明显感知空间信息。最后，针对噪声和语音同时存在于同一个时频点时，残留噪声现象更加严重的问题，设计了一种掩蔽映射滤波方法来代替传统的滤波求和运算，达到级联粗去噪、去混响和残留噪声抑制的目的。提出的模型Spatial-DCCRN在L3 DAS 22 Challenge数据集上超过了EaBNet、FasNet以及几个竞争模型。不仅在3D场景中，Spatial-DCCRN在多通道ConferencingSpeech 2021挑战数据集的多个评估指标中大幅优于最先进的（SOTA）模型MIMO-UNet。消融研究也证明了不同贡献的有效性。
英文摘要：
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.
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