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Software in Music Information Retrieval

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Challenges at the Interface of Data Analysis, Computer Science, and Optimization

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Abstract

Music Information Retrieval (MIR) software is often applied for the identification of rules classifying audio music pieces into certain categories, like e.g. genres. In this paper we compare the abilities of six MIR software packages in ten categories, namely operating systems, user interface, music data input, feature generation, feature formats, transformations and features, data analysis methods, visualization methods, evaluation methods, and further development. The overall rankings are derived from the estimated scores for the analyzed criteria.

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Author information

Authors and Affiliations

  1. Department of Statistics, TU Dortmund University, Dortmund, Germany

    Claus Weihs, Klaus Friedrichs & Markus Eichhoff

  2. Department of Computer Science, TU Dortmund University, Dortmund, Germany

    Igor Vatolkin

Authors
  1. Claus Weihs
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  2. Klaus Friedrichs
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  3. Markus Eichhoff
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  4. Igor Vatolkin
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Corresponding author

Correspondence to Claus Weihs .

Editor information

Editors and Affiliations

  1. Fak. Wirtschaftswissenschaften, Inst. Entscheidungstheorieund, Universität Karlsruhe (TH), Kaiserstr. 12, Karlsruhe, 76128, Germany

    Wolfgang A. Gaul

  2. Insitute for Information Systems, and Management (IISM), Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, Karlsruhe, 76131, Baden-Württemberg, Germany

    Andreas Geyer-Schulz

  3. , Information Systems, University ofHildesheim, Marienburger Platz 22, Hildesheim, 31141, Germany

    Lars Schmidt-Thieme

  4. Institute for Information Systems, and Management (IISM), Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, Karlsruhe, 76128, Germany

    Jonas Kunze

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© 2012 Springer-Verlag Berlin Heidelberg

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Weihs, C., Friedrichs, K., Eichhoff, M., Vatolkin, I. (2012). Software in Music Information Retrieval. In: Gaul, W., Geyer-Schulz, A., Schmidt-Thieme, L., Kunze, J. (eds) Challenges at the Interface of Data Analysis, Computer Science, and Optimization. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24466-7_43

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