Signal-domain representation of symbolic music for learning embedding spaces

Mathieu Prang (1) , Philippe Esling

1 IRCAM - Institut de Recherche et Coordination Acoustique/Musique

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.

Mots clés: Representation of music information; embedding spaces; Symbolic music generation; Representation of music information

Domaines: Intelligence artificielle [cs.AI]

Mathieu Prang, Philippe Esling. Signal-domain representation of symbolic music for learning embedding spaces. The 2020 Joint Conference on AI Music Creativity, Oct 2020, Stockholm, Sweden. ⟨hal-03329937⟩