  Universal sound separation consists of separating mixes with arbitrary sounds
of different types, and permutation invariant training (PIT) is used to train
source agnostic models that do so. In this work, we complement PIT with
adversarial losses but find it challenging with the standard formulation used
in speech source separation. We overcome this challenge with a novel
I-replacement context-based adversarial loss, and by training with multiple
discriminators. Our experiments show that by simply improving the loss (keeping
the same model and dataset) we obtain a non-negligible improvement of 1.4 dB
SI-SNRi in the reverberant FUSS dataset. We also find adversarial PIT to be
effective at reducing spectral holes, ubiquitous in mask-based separation
models, which highlights the potential relevance of adversarial losses for
source separation.
