  Recent deep learning approaches have shown great improvement in audio source
separation tasks. However, the vast majority of such work is focused on
improving average separation performance, often neglecting to examine or
control the distribution of the results. In this paper, we propose a simple,
unified gradient reweighting scheme, with a lightweight modification to bias
the learning process of a model and steer it towards a certain distribution of
results. More specifically, we reweight the gradient updates of each batch,
using a user-specified probability distribution. We apply this method to
various source separation tasks, in order to shift the operating point of the
models towards different objectives. We demonstrate different parameterizations
of our unified reweighting scheme can be used towards addressing several
real-world problems, such as unreliable separation estimates. Our framework
enables the user to control a robustness trade-off between worst and average
performance. Moreover, we experimentally show that our unified reweighting
scheme can also be used in order to shift the focus of the model towards being
more accurate for user-specified sound classes or even towards easier examples
in order to enable faster convergence.
