Notes on the Music
A social data infrastructure for music annotation
Weigl, David M.
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Goebl, Werner
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Baker, David J.
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Crawford, Tim
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Zubani, Federico
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Gkiokas, Aggelos
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Gutierrez, Nicolas F.
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Porter, Alastair
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Santos, Patricia
14f7e017-2673-49ed-b768-81dceefa7609
Abstract (English)
Beside transmitting musical meaning from composer to reader, symbolic music notation affords the dynamic addition of layers of information by annotation. This allows music scores to serve as rudimentary communication frameworks. Music encodings bring these affordances into the digital realm; though annotations may be represented as digital pen-strokes upon a score image, they must be captured using machine-interpretable semantics to fully benefit from this transformation. This is challenging, as annotators’ requirements are heterogeneous, varying both across different types of user (e.g., musician, scholar) and within these groups, depending on the specific use-case. A hypothetical all-encompassing tool catering to every conceivable annotation type, even if it were possible to build, would vastly complicate user interaction. This additional complexity would significantly increase cognitive load and impair usability, particularly in dynamic real-time usage contexts, e.g., live annotation during music rehearsal or performance. To address this challenge, we present a social data infrastructure that facilitates the creation of use-case specific annotation toolkits. Its components include a selectable-score module that supports customisable click-and-drag selection of score elements (e.g., notes, measures, directives); the Web Annotations data model, extended to support the creation of custom, Web-addressable annotation types supporting the specification and (re-)use of annotation palettes; and the Music Encoding and Linked Data (MELD) Javascript client library, used to build interfaces that map annotation types to rendering and interaction handlers. We have extended MELD to support the Solid platform for social Linked Data, allowing annotations to be privately stored in user-controlled Personal Online Datastores (Pods), or selectively shared or published. To demonstrate the feasibility of our proposed approach, we present annotation interfaces employing the outlined infrastructure in three distinct use-cases: scholarly communication; music rehearsal; and rating during music listening.
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