Signal-domain representation of symbolic music for learning embedding spaces
This addresses the challenge of representing complex symbolic music data for machine learning, offering a domain-specific improvement for music processing tasks.
The paper tackles the problem of learning efficient intermediate features from polyphonic musical scores by introducing a novel signal-domain representation that transforms scores into continuous signals, showing it leads to better reconstruction and disentangled features with improvements in metric properties and generation ability according to music theory.
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.