  In this paper, we propose a new paradigm to learn audio features for Music
Structure Analysis (MSA). We train a deep encoder to learn features such that
the Self-Similarity-Matrix (SSM) resulting from those approximates a
ground-truth SSM. This is done by minimizing a loss between both SSMs. Since
this loss is differentiable w.r.t. its input features we can train the encoder
in a straightforward way. We successfully demonstrate the use of this training
paradigm using the Area Under the Curve ROC (AUC) on the RWC-Pop dataset.
