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Searching and Classifying Affinities in a Web Music Collection

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Digital Libraries and Multimedia Archives (IRCDL 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 701))

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Abstract

Online music libraries available on the Web contain a large amount of audio content that is usually the result of digitization of analogue recordings or the direct acquisition of digital sources. The acquisition process is carried out by several persons and may last a number of years, thus it is likely that the same or similar audio content is present in different versions. This paper describes a number of possible similarities, which are called affinities, and presents a methodology to detect the kind of affinity from the automatic analysis and matching of the audio content.

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Acknowledgments

The author wishfully thanks the company LaCosa s.r.l. for granting access to a large digital audio collection, which has been the basis for the tests, and for providing useful insights on how interpreting the results.

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Authors and Affiliations

  1. Department of Cultural Heritage, University of Padua, Padua, Italy

    Nicola Orio

Authors
  1. Nicola Orio
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Correspondence to Nicola Orio .

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Editors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Padova, Padua, Italy

    Maristella Agosti

  2. Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Firenze, Florence, Italy

    Marco Bertini

  3. Dipartimento di Informatica, Università degli Studi di Bari, Bari, Italy

    Stefano Ferilli

  4. Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Firenze, Florence, Italy

    Simone Marinai

  5. Dipartimento dei Beni Culturali, Università degli Studi di Padova, Padua, Italy

    Nicola Orio

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Orio, N. (2017). Searching and Classifying Affinities in a Web Music Collection. In: Agosti, M., Bertini, M., Ferilli, S., Marinai, S., Orio, N. (eds) Digital Libraries and Multimedia Archives. IRCDL 2016. Communications in Computer and Information Science, vol 701. Springer, Cham. https://doi.org/10.1007/978-3-319-56300-8_6

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  • DOI: https://doi.org/10.1007/978-3-319-56300-8_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56299-5

  • Online ISBN: 978-3-319-56300-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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