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Social Media Retrieval pp 449–478Cite as

Exploiting Social Media for Music Information Retrieval

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Abstract

This chapter will first provide an introduction to information retrieval (IR) in general, before briefly explaining the research field of music information retrieval (MIR). Hereafter, we will discuss why and how social media mining (SMM) techniques can be beneficially employed in the context of MIR. More precisely, motivations for the common MIR tasks of music similarity computation, music popularity estimation, and auto-tagging musicwill be provided, and the current state-of-the-art in employing SMM techniques to these three tasks will be elaborated.

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Notes

  1. 1.

    CDDBis a web-based album identification service that returns, for a given unique disc identifier, meta-data like artist and album name, tracklist, or release year. This service is offered in a commercial version operated by Gracenote [38] as well as in an open source implementation named freeDB [36].

  2. 2.

    It is not clear whether the four mentioned publications make use of exactly the same data set. In any case, the authors emphasise that they only extract meta-data from OpenNap, but do not download any files.

  3. 3.

    In the meantime, last.fmhas extended its API with a Geo.getTopArtistsfunction that returns the top-played artists in a particular country.

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Authors and Affiliations

  1. Department of Computational Perception, Johannes Kepler University, Altenberger Straße 69, 4040, Linz, Austria

    Markus Schedl

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Editors and Affiliations

  1. Department of Electronic Engineering, Queen Mary University of London, Mile End Road, London, E1 4NS, United Kingdom

    Naeem Ramzan

  2. Director of Product Innovation, Search, Netflix, Winchester Circle 100, Los Gatos, 95032, California, USA

    Roelof van Zwol

  3. School of Integrated Technology, Yonsei University, 162-1 Songdo-dong, Yeonsu-gu, Incheon, 406-840, Korea, Republic of (South Korea)

    Jong-Seok Lee

  4. Institut für Telekommunikationssysteme, Technische Universität Berlin, Einsteinufer 17, Berlin, 10587, Germany

    Kai Clüver

  5. Media Computing Group, Microsoft Research, 555 108th Ave NE, Bellevue, 98004, Washington, USA

    Xian-Sheng Hua

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Schedl, M. (2013). Exploiting Social Media for Music Information Retrieval. In: Ramzan, N., van Zwol, R., Lee, JS., Clüver, K., Hua, XS. (eds) Social Media Retrieval. Computer Communications and Networks. Springer, London. https://doi.org/10.1007/978-1-4471-4555-4_20

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