  Neural networks have recently become the dominant approach to sound
separation. Their good performance relies on large datasets of isolated
recordings. For speech and music, isolated single channel data are readily
available; however the same does not hold in the multi-channel case, and with
most other sound classes. Multi-channel methods have the potential to
outperform single channel approaches as they can exploit both spatial and
spectral features, but the lack of training data remains a challenge. We
propose unsupervised improved minimum variation distortionless response
(UIMVDR), which enables multi-channel separation to leverage in-the-wild
single-channel data through unsupervised training and beamforming. Results show
that UIMVDR generalizes well and improves separation performance compared to
supervised models, particularly in cases with limited supervised data. By using
data available online, it also reduces the effort required to gather data for
multi-channel approaches.
