Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
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An e-Research approach to Web-scale music analysis

    The growing quantity of digital recorded music available in large-scale resources such as the Internet archive provides an important new resource for musical analysis. An e-Research approach has been adopted in order to create a very substantive web-accessible corpus of musical analyses in a common framework for use by music scholars, students and beyond, and to establish a methodology and tooling that will enable others to add to the resource in the future. The enabling infrastructure brings together scientific workflow and Semantic Web technologies with a set of algorithms and tools for extracting features from recorded music. It has been used to deliver a prototype system, described here, that demonstrates the utility of Linked Data for enhancing the curation of collections of music signal data for analysis and publishing results that can be simply and readily correlated to these and other sources. This paper describes the motivation, infrastructure design and the proof-of-concept case study and reflects on emerging e-Research practice as researchers embrace the scale of the Web.

    Footnotes

    One contribution of 12 to a Theme Issue ‘e-Science: novel research, new science and enduring impact’.

    References

    • 1
      Hey T., Tansley S.& Tolle K.. 2009The fourth paradigm: data-intensive scientific discoveryRedmond, WAMicrosoft Research. Google Scholar
    • 2
      Tarte S. M., Wallom D. C. H., Hu P., Tang K.& Ma T.. 2009An image processing portal and web-service for the study of ancient documents. Int. Conf. on e-Science and Grid Computing14-19Los Alamitos, CAIEEE Computer Society(doi:10.1109/e-Science.2009.10). Google Scholar
    • 3
      Taylor K. R., Gledhill R. J., Essex J. W., Frey J. G., Harris S. W.& De Roure D. C.. 2006Bringing chemical data onto the semantic web. J. Chem. Inf. Model. 46, 939-952(doi:10.1021/ci050378m). Crossref, PubMed, ISIGoogle Scholar
    • 4
      Bizer C., Heath T.& Berners-Lee T.. 2009LINKED DATA: the story so far. Int. J. Semantic Web Inform. Syst. 5, 1-22(doi:10.4018/jswis.2009081901). ISIGoogle Scholar
    • 5
      Bechhofer S., et al.2010Why Linked Data is not enough for scientists. 6th IEEE Int. Conf. on e-Science300-307Los Alamitos, CAIEEE Computer Society(doi:10.1109/eScience.2010.21). Google Scholar
    • 6
      Downie J. S.. 2008The Music Information Retrieval Evaluation Exchange (2005–2007): a window into music information retrieval research. Acoust. Sci. Technol. 29, 247-255(doi:10.1250/ast.29.247). CrossrefGoogle Scholar
    • 7
      Llorà X., Ács B., Auvil L. S., Capitanu B., Welge M. E.& Goldberg D. E.. 2008Meandre: semantic-driven data-intensive flows in the clouds. 4th IEEE Int. Conf. on e-Science, Indianapolis, IN, 7–12 December 2008238-245IEEE(doi:10.1109/eScience.2008.172). Google Scholar
    • 8
      Raimond Y., Abdallah S., Sandler M.& Giasson F.. 2007The Music Ontology. Proc. Int. Conf. on Music Information Retrieval, Vienna, Austria, 23–30 September 2007417-422The International Society for Music Information Retrieval. Google Scholar
    • 9
      West K., Kumar A., Shirk A., Zhu G., Downie J., Ehmann A.& Bay M.. 2010The Networked Environment for Music Analysis (NEMA). 6th IEEE World Congress on Services, Miami, FL, 5–10 July 2010314-317IEEE(doi:10.1109/SERVICES.2010.113). CrossrefGoogle Scholar
    • 10
      McKay C., Burgoyne J. A., Thompson J.& Fujinaga I.. 2009Using ACE XML 2.0 to store and share feature, instance and class data for musical classification. Proc. Int. Society for Music Information Retrieval Conf. 2009, Kobe, Japan, 26–30 October 2009303-308The International Society for Music Information Retrieval. Google Scholar
    • 11
      Raimond Y.& Sandler M.. 2008A web of musical information. Proc. Int. Conf. on Music Information Retrieval 2008, Philadelphia, PA, 14–18 September 2008263-268The International Society for Music Information Retrieval. Google Scholar
    • 12
      Cannam C., Landone C., Sandler M.& Bello J.. 2006The sonic visualiser: a visualisation platform for semantic descriptors from musical signals. Proc. 7th Int. Conf. on Music Information Retrieval, Victoria, BC, 8–12 October 2006324-327The International Society for Music Information Retrieval. Google Scholar
    • 13
      Hartig O.& Zhao J.. 2010Publishing and consuming provenance metadata on the Web of Linked Data. Provenance and annotation of data and processes, McGuinness D., Michaelis J.& Moreau L.Lecture Notes in Computer Science, no. 637878-90Berlin, GermanySpringer(doi:1007/-642-17819-1_10). Google Scholar
    • 14
      Lagoze C.& Van de Sompel H.. 2008Object reuse and exchange (OAI-ORE). Technical report. Open Archives Initiative. See http://www.openarchives.org/ore/1.0. Google Scholar
    • 15
      De Roure D., Goble C.& Stevens R.. 2009The design and realisation of the myExperiment virtual research environment for social sharing of workflows. Future Gener. Comput. Syst. 25, 561-567(doi:10.1016/j.future.2008.06.010). Crossref, ISIGoogle Scholar
    • 16
      Auer S., Bizer C., Kobilarov G., Lehmann J.& Ives Z.. 2007DBpedia: a nucleus for a web of open data. 6th Int. Semantic Web Conf.11-15Berlin, GermanySpringer. Google Scholar
    • 17
      Richardson L.& Ruby S.. 2007RESTful Web ServicesSebastopol, CA: O'Reilly & Associates. See http://oreilly.com/catalog/9780596529260. Google Scholar
    • 18
      Fielding R. T.. 2000Architectural styles and the design of network-based software architectures. PhD thesis, Information and Computer Science, University of California, Irvine, CA, USA. Google Scholar
    • 19
      Sauermann L.& Cyganiak R.. 2007Cool URIs for the Semantic WebC Semantic Web Education and Outreach Interest Group Note. See http://www.w3.org/TR/2007/WD-cooluris-20071217/. Google Scholar
    • 20
      McKay C.& Fujinaga I.. 2009jMIR tools for automatic music classification. Proc. Int. Computer Music Conf. 2009, Kobe, Japan, 26–30 October 200965-68The International Society for Music Information Retrieval. Google Scholar
    • 21
      Jacobson K., Raimond Y.& Sandler M.. 2009An ecosystem for transparent music similarity in an open world. Proc. Int. Conf. on Music Information Retrieval 2009, Kobe, Japan, 26–30 October 200933-38The International Society for Music Information Retrieval. Google Scholar
    • 22
      Berenzweig A., Logan B., Ellis D.& Whitman B.. 2004A large-scale evaluation of acoustic and subjective music-similarity measures. Comput. Music J. 28, 63-76(doi:10.1162/014892604323112257). Crossref, ISIGoogle Scholar
    • 23
      Page K. R., Fields B., Nagel B. J., O'Neill G., De Roure D. C.& Crawford T.. 2010Semantics for music analysis through linked data: how country is my country?6th IEEE Int. Conf. on e-Science41-48Los Alamitos, CAIEEE Computer Society(doi:10.1109/eScience.2010.49). Google Scholar
    • 24
      Page K. R., De Roure D. C.& Martinez K.. 2011Rest and Linked Data: a match made for domain driven development?2nd Int. Workshop on REST FUL designNew York, NYACM. Google Scholar