  A key aspect of machine learning models lies in their ability to learn
efficient intermediate features. However, the input representation plays a
crucial role in this process, and polyphonic musical scores remain a
particularly complex type of information. In this paper, we introduce a novel
representation of symbolic music data, which transforms a polyphonic score into
a continuous signal. We evaluate the ability to learn meaningful features from
this representation from a musical point of view. Hence, we introduce an
evaluation method relying on principled generation of synthetic data. Finally,
to test our proposed representation we conduct an extensive benchmark against
recent polyphonic symbolic representations. We show that our signal-like
representation leads to better reconstruction and disentangled features. This
improvement is reflected in the metric properties and in the generation ability
of the space learned from our signal-like representation according to music
theory properties.
