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.

我们提出了一种方法，在给定输入混合物和目标源的部分语义信息的情况下，训练模型提取额外的语义数据，然后利用这个预训练模型来改善解耦的多条件分离网络的分离性能，实验证明我们的方法达到了接近具有完整语义信息的理想模型的分离性能，并且相当于最佳专门单一条件模型的性能水平，提供了一种易于使用的选择。