  We present a novel model designed for resource-efficient multichannel speech
enhancement in the time domain, with a focus on low latency, lightweight, and
low computational requirements. The proposed model incorporates explicit
spatial and temporal processing within deep neural network (DNN) layers.
Inspired by frequency-dependent multichannel filtering, our spatial filtering
process applies multiple trainable filters to each hidden unit across the
spatial dimension, resulting in a multichannel output. The temporal processing
is applied over a single-channel output stream from the spatial processing
using a Long Short-Term Memory (LSTM) network. The output from the temporal
processing stage is then further integrated into the spatial dimension through
elementwise multiplication. This explicit separation of spatial and temporal
processing results in a resource-efficient network design. Empirical findings
from our experiments show that our proposed model significantly outperforms
robust baseline models while demanding far fewer parameters and computations,
while achieving an ultra-low algorithmic latency of just 2 milliseconds.
