Full Paper

Towards the Representation of Chinese Traditional Music: A State of the Art Review of Music Metadata Standards

Mi Tian , György Fazekas , Dawn Black , Mark Sandler
Download PDF Read Online
SCOPUS ID: 84891276909 Wikidata ID: Q68239505
Abstract

This paper examines existing metadata standards for describing music related information in the context of Chinese music tradition. With most research attention focussing on n music, research into computational methods and knowledge representation for world music is still in its infancy. Following the introduction of symbolic elements in the Chinese traditional system, a comparison between these elements and the expressiveness of some prevailing metadata models and standards including Semantic Web ontologies is presented.

Author information

Mi Tian
Centre for Digital Music, School of Electronic Engineering and Computer Science, Queen Mary University of London, UK
Google Scholar Semantic Scholar
György Fazekas
Centre for Digital Music, School of Electronic Engineering and Computer Science, Queen Mary University of London, UK
Google Scholar Semantic Scholar
Dawn Black
Centre for Digital Music, School of Electronic Engineering and Computer Science, Queen Mary University of London, UK
Google Scholar Semantic Scholar
Mark Sandler
School of Electronic Engineering and Computer Science, Queen Mary University of London, UK
Google Scholar Semantic Scholar

Cite this article

Tian, M., Fazekas, G., Black, D., & Sandler, M. (2013). Towards the Representation of Chinese Traditional Music: A State of the Art Review of Music Metadata Standards. International Conference on Dublin Core and Metadata Applications, 2013. https://doi.org/10.23106/dcmi.952136113

DOI : 10.23106/dcmi.952136113

CC-0 Logo Metadata and citations of this article is published under the Creative Commons Zero Universal Public Domain Dedication (CC0), allowing unrestricted reuse. Anyone can freely use the metadata from DCPapers articles for any purpose without limitations.
CC-BY Logo This article full-text is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license allows use, sharing, adaptation, distribution, and reproduction in any medium or format, provided that appropriate credit is given to the original author(s) and the source is cited.