  Due to advances in deep learning, the performance of automatic beat and
downbeat tracking in musical audio signals has seen great improvement in recent
years. In training such deep learning based models, data augmentation has been
found an important technique. However, existing data augmentation methods for
this task mainly target at balancing the distribution of the training data with
respect to their tempo. In this paper, we investigate another approach for data
augmentation, to account for the composition of the training data in terms of
the percussive and non-percussive sound sources. Specifically, we propose to
employ a blind drum separation model to segregate the drum and non-drum sounds
from each training audio signal, filtering out training signals that are
drumless, and then use the obtained drum and non-drum stems to augment the
training data. We report experiments on four completely unseen test sets,
validating the effectiveness of the proposed method, and accordingly the
importance of drum sound composition in the training data for beat and downbeat
tracking.
