  A cover song, by definition, is a new performance or recording of a
previously recorded, commercially released song. It may be by the original
artist themselves or a different artist altogether and can vary from the
original in unpredictable ways including key, arrangement, instrumentation,
timbre and more. In this work we propose a novel approach to learning audio
representations for the task of cover song detection. We train a neural
architecture on tens of thousands of cover-song audio clips and test it on a
held out set. We obtain a mean precision@1 of 65% over mini-batches, ten times
better than random guessing. Our results indicate that Siamese network
configurations show promise for approaching the cover song identification
problem.
