  In this paper we study deep learning-based music source separation, and
explore using an alternative loss to the standard spectrogram pixel-level L2
loss for model training. Our main contribution is in demonstrating that adding
a high-level feature loss term, extracted from the spectrograms using a VGG
net, can improve separation quality vis-a-vis a pure pixel-level loss. We show
this improvement in the context of the MMDenseNet, a State-of-the-Art deep
learning model for this task, for the extraction of drums and vocal sounds from
songs in the musdb18 database, covering a broad range of western music genres.
We believe that this finding can be generalized and applied to broader machine
learning-based systems in the audio domain.
