  In this paper, we describe the work that we have done to participate in Task1
of the ConferencingSpeech2021 challenge. This task set a goal to develop the
solution for multi-channel speech enhancement in a real-time manner. We propose
a novel system for streaming speech enhancement. We employ Wave-U-Net
architecture with temporal convolutions in encoder and decoder. We incorporate
self-attention in the decoder to apply attention mask retrieved from
skip-connection on features from down-blocks. We explore history cache
mechanisms that work like hidden states in recurrent networks and implemented
them in proposal solution. It helps us to run an inference with chunks length
40ms and Real-Time Factor 0.4 with the same precision.
