  Most contemporary music tagging systems rely on large volumes of annotated
data. As an alternative, we investigate the extent to which synthetically
generated music excerpts can improve tagging systems when only small annotated
collections are available. To this end, we release GTZAN-synth, a synthetic
dataset that follows the taxonomy of the well-known GTZAN dataset while being
ten times larger in data volume. We first observe that simply adding this
synthetic dataset to the training split of GTZAN does not result into
performance improvements. We then proceed to investigating domain adaptation,
transfer learning and fine-tuning strategies for the task at hand and draw the
conclusion that the last two options yield an increase in accuracy. Overall,
the proposed approach can be considered as a first guide in a promising field
for future research.
