  A novel model was recently proposed by Schulze-Forster et al. in [1] for
unsupervised music source separation. This model allows to tackle some of the
major shortcomings of existing source separation frameworks. Specifically, it
eliminates the need for isolated sources during training, performs efficiently
with limited data, and can handle homogeneous sources (such as singing voice).
But, this model relies on an external multipitch estimator and incorporates an
Ad hoc voice assignment procedure. In this paper, we propose to extend this
framework and to build a fully differentiable model by integrating a multipitch
estimator and a novel differentiable assignment module within the core model.
We show the merits of our approach through a set of experiments, and we
highlight in particular its potential for processing diverse and unseen data.
