  In this work, we propose a new model called triple-path attentive recurrent
network (TPARN) for multichannel speech enhancement in the time domain. TPARN
extends a single-channel dual-path network to a multichannel network by adding
a third path along the spatial dimension. First, TPARN processes speech signals
from all channels independently using a dual-path attentive recurrent network
(ARN), which is a recurrent neural network (RNN) augmented with self-attention.
Next, an ARN is introduced along the spatial dimension for spatial context
aggregation. TPARN is designed as a multiple-input and multiple-output
architecture to enhance all input channels simultaneously. Experimental results
demonstrate the superiority of TPARN over existing state-of-the-art approaches.
