  Recent approaches in source separation leverage semantic information about
their input mixtures and constituent sources that when used in conditional
separation models can achieve impressive performance. Most approaches along
these lines have focused on simple descriptions, which are not always useful
for varying types of input mixtures. In this work, we present an approach in
which a model, given an input mixture and partial semantic information about a
target source, is trained to extract additional semantic data. We then leverage
this pre-trained model to improve the separation performance of an uncoupled
multi-conditional separation network. Our experiments demonstrate that the
separation performance of this multi-conditional model is significantly
improved, approaching the performance of an oracle model with complete semantic
information. Furthermore, our approach achieves performance levels that are
comparable to those of the best performing specialized single conditional
models, thus providing an easier to use alternative.
