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Mining Oral History Collections Using Music Information Retrieval Methods

Webb, Sharon et al.

Music reference services quarterly: MRSQ. Volume 20:Number 3/4 (2017); pp 168-183 -- Routledge, Taylor & Francis Group

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  • Title:
    Mining Oral History Collections Using Music Information Retrieval Methods
  • Author: Webb, Sharon;
    Kiefer, Chris;
    Jackson, Ben;
    Baker, James;
    Eldridge, Alice
  • Found In: Music reference services quarterly: MRSQ. Volume 20:Number 3/4 (2017); pp 168-183
  • Journal Title: Music reference services quarterly: MRSQ
  • Subjects: Music libraries--Periodicals; Music--Periodicals; Reference services (Libraries)--Periodicals; Oral History--music information retrieval--audio analysis--digital history; Dewey: 026.78
  • Rights: legaldeposit
  • Publication Details: Routledge, Taylor & Francis Group
  • Abstract: ABSTRACT:

    Recent work at the Sussex Humanities Lab, a digital humanities research program at the University of Sussex, has sought to address an identified gap in the provision and use of audio feature analysis for spoken word collections. Traditionally, oral history methodologies and practices have placed emphasis on working with transcribed textual surrogates, rather than the digital audio files created during the interview process. This provides a pragmatic access to the basic semantic content, but obviates access to other potentially meaningful aural information; our work addresses the potential for methods to explore this extra-semantic information, by working with the audio directly. Audio analysis tools, such as those developed within the established field of Music Information Retrieval (MIR), provide this opportunity. This article describes the application of audio analysis techniques and methods to spoken word collections. We demonstrate an approach using freely available audio and data analysis tools, which have been explored and evaluated in two workshops. We hope to inspire new forms of content analysis which complement semantic analysis with investigation into the more nuanced properties carried in audio signals.


  • Identifier: System Number: LDEAvdc_100075274193.0x000001; Journal ISSN: 1058-8167; 10.1080/10588167.2017.1404307
  • Publication Date: 2017
  • Physical Description: Electronic
  • Shelfmark(s): ELD Digital store

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