Music similarity and retrieval

  • Authors:
  • Peter Knees;Markus Schedl

  • Affiliations:
  • Johannes Kepler University Linz, Linz, Austria;Johannes Kepler University Linz, Linz, Austria

  • Venue:
  • Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
  • Year:
  • 2013

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Abstract

This tutorial serves as an introductory course to the field of and state-of-the-art in music information retrieval (MIR) and in particular to music similarity estimation which is an essential component of music retrieval. Apart from explaining approaches that estimate similarity based on acoustic properties of an audio signal, we review methods that exploit (mostly textual) meta-data from the Web to build representations of music then used for similarity calculation. Additionally, topics such as (large-scale) music indexing, information extraction for music, personalization in music retrieval, and evaluation of MIR systems are addressed.