  In recent years, waveform-mapping-based speech enhancement (SE) methods have
garnered significant attention. These methods generally use a deep learning
model to directly process and reconstruct speech waveforms. Because both the
input and output are in waveform format, the waveform-mapping-based SE methods
can overcome the distortion caused by imperfect phase estimation, which may be
encountered in spectral-mapping-based SE systems. So far, most
waveform-mapping-based SE methods have focused on single-channel tasks. In this
paper, we propose a novel fully convolutional network (FCN) with Sinc and
dilated convolutional layers (termed SDFCN) for multichannel SE that operates
in the time domain. We also propose an extended version of SDFCN, called the
residual SDFCN (termed rSDFCN). The proposed methods are evaluated on two
multichannel SE tasks, namely the dual-channel inner-ear microphones SE task
and the distributed microphones SE task. The experimental results confirm the
outstanding denoising capability of the proposed SE systems on both tasks and
the benefits of using the residual architecture on the overall SE performance.
