  Audio source separation is a difficult machine learning problem and
performance is measured by comparing extracted signals with the component
source signals. However, if separation is motivated by the ultimate goal of
re-mixing then complete separation is not necessary and hence separation
difficulty and separation quality are dependent on the nature of the re-mix.
Here, we use a convolutional deep neural network (DNN), trained to estimate
'ideal' binary masks for separating voice from music, to perform re-mixing of
the vocal balance by operating directly on the individual magnitude components
of the musical mixture spectrogram. Our results demonstrate that small changes
in vocal gain may be applied with very little distortion to the ultimate
re-mix. Our method may be useful for re-mixing existing mixes.
