  In music source separation, a standard training data augmentation procedure
is to create new training samples by randomly combining instrument stems from
different songs. These random mixes have mismatched characteristics compared to
real music, e.g., the different stems do not have consistent beat or tonality,
resulting in a cacophony. In this work, we investigate why random mixing is
effective when training a state-of-the-art music source separation model in
spite of the apparent distribution shift it creates. Additionally, we examine
why performance levels off despite potentially limitless combinations, and
examine the sensitivity of music source separation performance to differences
in beat and tonality of the instrumental sources in a mixture.
