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An e-Research approach to Web-scale music analysis.

David De Roure1, Kevin R Page, Benjamin Fields

  • 1Oxford e-Research Centre, University of Oxford, Oxford, UK. david.deroure@oerc.ox.ac.uk

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|July 20, 2011
PubMed
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This study introduces an e-Research approach for musical analysis, creating a web-accessible corpus of music data. It utilizes Semantic Web technologies and algorithms to extract features, enabling enhanced music data curation and analysis.

Area of Science:

  • Digital Humanities
  • Music Information Retrieval
  • Computational Musicology

Background:

  • Large-scale digital music archives offer new opportunities for musical analysis.
  • Existing resources lack a common framework for collaborative analysis and data sharing.
  • The need for robust methodologies and tools to manage and analyze extensive music datasets is growing.

Purpose of the Study:

  • To develop an e-Research infrastructure for creating a web-accessible corpus of musical analyses.
  • To establish a methodology and tooling for future contributions to the corpus.
  • To demonstrate the utility of Linked Data for music signal data curation and analysis.

Main Methods:

  • Adoption of an e-Research approach integrating scientific workflow and Semantic Web technologies.

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  • Development of algorithms and tools for extracting features from recorded music.
  • Implementation of a prototype system utilizing Linked Data for data correlation and result publication.
  • Main Results:

    • Creation of a web-accessible corpus of musical analyses in a common framework.
    • Demonstration of a prototype system showcasing Linked Data's utility in music data curation.
    • Establishment of a methodology and tooling for future data enrichment and analysis.

    Conclusions:

    • The developed infrastructure facilitates enhanced curation and analysis of music signal data.
    • Linked Data proves effective in correlating music analysis results with various data sources.
    • This approach supports emerging e-Research practices for large-scale web-based research.