  We introduce a time-domain framework for efficient multichannel speech
enhancement, emphasizing low latency and computational efficiency. This
framework incorporates two compact deep neural networks (DNNs) surrounding a
multichannel neural Wiener filter (NWF). The first DNN enhances the speech
signal to estimate NWF coefficients, while the second DNN refines the output
from the NWF. The NWF, while conceptually similar to the traditional
frequency-domain Wiener filter, undergoes a training process optimized for
low-latency speech enhancement, involving fine-tuning of both analysis and
synthesis transforms. Our research results illustrate that the NWF output,
having minimal nonlinear distortions, attains performance levels akin to those
of the first DNN, deviating from conventional Wiener filter paradigms. Training
all components jointly outperforms sequential training, despite its simplicity.
Consequently, this framework achieves superior performance with fewer
parameters and reduced computational demands, making it a compelling solution
for resource-efficient multichannel speech enhancement.
