Critical Semantic Properties of Music Notation Datasets

TL;DR

This paper explores the semantic properties of music notation datasets, modeling notation as a network of transformations and analyzing how digital encoding choices affect maintenance and extension costs.

Contribution

It introduces a formal framework for understanding the semantic properties of music notation datasets and their impact on data management tasks.

Findings

Semantic properties influence data maintenance costs

Encoding choices affect dataset extensibility

Network modeling clarifies notation semantics

Abstract

The semantics of notation systems can naturally be meta-modelled as a network of transformations, starting with the syntactic elements of the notation and ending with the parameters of an execution. In this context, a digital encoding format for music notation can be seen as selecting a subset of the data nodes of this network for storage, leaving others to evaluation. For such a selection, semantic properties are defined which have impact on the practical costs of maintenance, migration, extension, etc.