  Ultra-lightweight model design is an important topic for the deployment of
existing speech enhancement and source separation techniques on low-resource
platforms. Various lightweight model design paradigms have been proposed in
recent years; however, most models still suffer from finding a balance between
model size, model complexity, and model performance. In this paper, we propose
the group communication with context codec (GC3) design to decrease both model
size and complexity without sacrificing the model performance. Group
communication splits a high-dimensional feature into groups of low-dimensional
features and applies a module to capture the inter-group dependency. A model
can then be applied to the groups in parallel with a significantly smaller
width. A context codec is applied to decrease the length of a sequential
feature, where a context encoder compresses the temporal context of local
features into a single feature representing the global characteristics of the
context, and a context decoder decompresses the transformed global features
back to the context features. Experimental results show that GC3 can achieve on
par or better performance than a wide range of baseline architectures with as
small as 2.5% model size.
