  Deep learning approaches have recently achieved impressive performance on
both audio source separation and sound classification. Most audio source
separation approaches focus only on separating sources belonging to a
restricted domain of source classes, such as speech and music. However, recent
work has demonstrated the possibility of "universal sound separation", which
aims to separate acoustic sources from an open domain, regardless of their
class. In this paper, we utilize the semantic information learned by sound
classifier networks trained on a vast amount of diverse sounds to improve
universal sound separation. In particular, we show that semantic embeddings
extracted from a sound classifier can be used to condition a separation
network, providing it with useful additional information. This approach is
especially useful in an iterative setup, where source estimates from an initial
separation stage and their corresponding classifier-derived embeddings are fed
to a second separation network. By performing a thorough hyperparameter search
consisting of over a thousand experiments, we find that classifier embeddings
from clean sources provide nearly one dB of SNR gain, and our best iterative
models achieve a significant fraction of this oracle performance, establishing
a new state-of-the-art for universal sound separation.
