  Representation learning focused on disentangling the underlying factors of
variation in given data has become an important area of research in machine
learning. However, most of the studies in this area have relied on datasets
from the computer vision domain and thus, have not been readily extended to
music. In this paper, we present a new symbolic music dataset that will help
researchers working on disentanglement problems demonstrate the efficacy of
their algorithms on diverse domains. This will also provide a means for
evaluating algorithms specifically designed for music. To this end, we create a
dataset comprising of 2-bar monophonic melodies where each melody is the result
of a unique combination of nine latent factors that span ordinal, categorical,
and binary types. The dataset is large enough (approx. 1.3 million data points)
to train and test deep networks for disentanglement learning. In addition, we
present benchmarking experiments using popular unsupervised disentanglement
algorithms on this dataset and compare the results with those obtained on an
image-based dataset.
