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Classification in music research

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Abstract

Since a few years, classification in music research is a very broad and quickly growing field. Most important for adequate classification is the knowledge of adequate observable or deduced features on the basis of which meaningful groups or classes can be distinguished. Unsupervised classification additionally needs an adequate similarity or distance measure grouping is to be based upon. Evaluation of supervised learning is typically based on the error rates of the classification rules. In this paper we first discuss typical problems and possible influential features derived from signal analysis, mental mechanisms or concepts, and compositional structure. Then, we present typical solutions of such tasks related to music research, namely for organization of music collections, transcription of music signals, cognitive psychology of music, and compositional structure analysis.

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Authors and Affiliations

  1. Fachbereich Statistik, Universität Dortmund, 44221, Dortmund, Germany

    Claus Weihs & Uwe Ligges

  2. Siemens Corporate Research, 755 College Road East, Princeton, NJ, 08540, USA

    Fabian Mörchen

  3. Department of Computing, Goldsmiths College, University of London, London, UK

    Daniel Müllensiefen

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  1. Claus Weihs
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  2. Uwe Ligges
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  3. Fabian Mörchen
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  4. Daniel Müllensiefen
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Correspondence to Claus Weihs.

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Weihs, C., Ligges, U., Mörchen, F. et al. Classification in music research. ADAC 1, 255–291 (2007). https://doi.org/10.1007/s11634-007-0016-x

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