  Model size and complexity remain the biggest challenges in the deployment of
speech enhancement and separation systems on low-resource devices such as
earphones and hearing aids. Although methods such as compression, distillation
and quantization can be applied to large models, they often come with a cost on
the model performance. In this paper, we provide a simple model design paradigm
that explicitly designs ultra-lightweight models without sacrificing the
performance. Motivated by the sub-band frequency-LSTM (F-LSTM) architectures,
we introduce the group communication (GroupComm), where a feature vector is
split into smaller groups and a small processing block is used to perform
inter-group communication. Unlike standard F-LSTM models where the sub-band
outputs are concatenated, an ultra-small module is applied on all the groups in
parallel, which allows a significant decrease on the model size. Experiment
results show that comparing with a strong baseline model which is already
lightweight, GroupComm can achieve on par performance with 35.6 times fewer
parameters and 2.3 times fewer operations.
