  Annotating musical beats is a very long and tedious process. In order to
combat this problem, we present a new self-supervised learning pretext task for
beat tracking and downbeat estimation. This task makes use of Spleeter, an
audio source separation model, to separate a song's drums from the rest of its
signal. The first set of signals are used as positives, and by extension
negatives, for contrastive learning pre-training. The drum-less signals, on the
other hand, are used as anchors. When pre-training a fully-convolutional and
recurrent model using this pretext task, an onset function is learned. In some
cases, this function is found to be mapped to periodic elements in a song. We
find that pre-trained models outperform randomly initialized models when a beat
tracking training set is extremely small (less than 10 examples). When this is
not the case, pre-training leads to a learning speed-up that causes the model
to overfit to the training set. More generally, this work defines new
perspectives in the realm of musical self-supervised learning. It is notably
one of the first works to use audio source separation as a fundamental
component of self-supervision.
