Modeling Music as Synchronized Time Series: Application to Music Score Collections

Raphaël Fournier-S'Niehotta (1) , Philippe Rigaux (1) , Nicolas Travers (1)

1 CEDRIC - VERTIGO - CEDRIC. Données complexes, apprentissage et représentations

Music is a temporal organization of sounds, and we can therefore assume that any music representation has a structure that reflects some conceptual principles. This structure is hardly explicitly accessible in many encodings, such as, for instance, audio files. However, it appears much more clearly in the language of music notation. We propose to use the music notation language as a framework to model and manipulate the content of digital music objects, whatever their specific encoding may be. We describe an algebra that relies on this structured music representation to extract, restructure , and search such objects. The data model leverages the hidden structure of digital music encodings to enable powerful manipulations of their content. We apply the model to collections of music scores. We describe a system, based on an extension of XQuery with our algebra, that provides search, reorganization, and extraction functionalities on top of large collections of XML-encoded digital scores. Beyond its application to music objects, our work shows how one can rely on a structured content embedded in a complex XML encoding to develop robust collection management tools with minimal implementation effort.

Domaines: Intelligence artificielle [cs.AI]

Raphaël Fournier-S'Niehotta, Philippe Rigaux, Nicolas Travers. Modeling Music as Synchronized Time Series: Application to Music Score Collections. Information Systems, 2018, 73, pp.35-49. ⟨10.1016/j.is.2017.12.003⟩. ⟨hal-02435620⟩