We investigate which loss functions provide better separations via
benchmarking an extensive set of those for music source separation.
To that end, we first survey the most representative audio source
separation losses we identified, to later consistently benchmark them
in a controlled experimental setup. We also explore using such losses
as evaluation metrics, via cross-correlating them with the results of
a subjective test. Based on the observation that the standard signal-
to-distortion ratio metric can be misleading in some scenarios, we
study alternative evaluation metrics based on the considered losses.