  Ad-hoc distributed microphone environments, where microphone locations and
numbers are unpredictable, present a challenge to traditional deep learning
models, which typically require fixed architectures. To tailor deep learning
models to accommodate arbitrary array configurations, the
Transform-Average-Concatenate (TAC) layer was previously introduced. In this
work, we integrate TAC layers with dual-path transformers for speech separation
from two simultaneous talkers in realistic settings. However, the distributed
nature makes it hard to fuse information across microphones efficiently.
Therefore, we explore the efficacy of blindly clustering microphones around
sources of interest prior to enhancement. Experimental results show that this
deep cluster-informed approach significantly improves the system's capacity to
cope with the inherent variability observed in ad-hoc distributed microphone
environments.
