  The integration of additional side information to improve music source
separation has been investigated numerous times, e.g., by adding features to
the input or by adding learning targets in a multi-task learning scenario.
These approaches, however, require additional annotations such as musical
scores, instrument labels, etc. in training and possibly during inference. The
available datasets for source separation do not usually provide these
additional annotations. In this work, we explore transfer learning strategies
to incorporate VGGish features with a state-of-the-art source separation model;
VGGish features are known to be a very condensed representation of audio
content and have been successfully used in many MIR tasks. We introduce three
approaches to incorporate the features, including two latent space
regularization methods and one naive concatenation method. Experimental results
show that our proposed approaches improve several evaluation metrics for music
source separation.
