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Piano and Guitar Tone Distinction Based on Extended Feature Analysis

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Classification and Data Mining

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Abstract

In this work single piano and guitar tones are distinguished by means of various features of the music time series. In a first study, three different kinds of high-level features and MFCC are taken into account to classify the piano and guitar tones. The features are called high-level because they try to reflect the physical structure of a musical instrument on temporal and spectral levels. In our study, three spectral features and one temporal feature are used for the classification task. The spectral features characterize the distribution of overtones, the temporal feature the energy of a tone. In a second study as many low level and the high level features as possible proposed in the literature are combined for the classification task.

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Author information

Authors and Affiliations

  1. Chair of Computational Statistics, TU Dortmund, Dortmund, Germany

    Markus Eichhoff & Claus Weihs

  2. Chair of Algorithm Engineering, TU Dortmund, Dortmund, Germany

    Igor Vatolkin

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

Correspondence to Markus Eichhoff .

Editor information

Editors and Affiliations

  1. Department of Statistics, Università degli Studi di Firenze, Viale G.B. Morgagni 59, Firenze, 50134, Italy

    Antonio Giusti

  2. Fakultät für Informatik, und Mathematik, Universität Passau, Innstr. 33, Passau, 94030, Germany

    Gunter Ritter

  3. Sapienza", Department of Statistics, University of Rome "La, Piazzale Aldo Moro 5, Rome, 00185, Italy

    Maurizio Vichi

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Eichhoff, M., Vatolkin, I., Weihs, C. (2013). Piano and Guitar Tone Distinction Based on Extended Feature Analysis. In: Giusti, A., Ritter, G., Vichi, M. (eds) Classification and Data Mining. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28894-4_26

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