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Music Learning: Automatic Music Composition and Singing Voice Assessment

  • Chapter
Springer Handbook of Systematic Musicology

Part of the book series: Springer Handbooks ((SHB))

Abstract

Traditionally, singing skills are learned and improved by means of the supervised rehearsal of a set of selected exercises. A music teacher evaluates the user's performance and recommends new exercises according to the user's evolution.

In this chapter, the goal is to describe a virtual environment that partially resembles the traditional music learning process and the music teacher's role, allowing for a complete interactive self-learning process.

An overview of the complete chain of an interactive singing-learning system including tools and concrete techniques will be presented. In brief, first, the system should provide a set of training exercises. Then, it should assess the user's performance. Finally, the system should be able to provide the user with new exercises selected or created according to the results of the evaluation.

Following this scheme, methods for the creation of user-adapted exercises and the automatic evaluation of singing skills will be presented. A technique for the dynamical generation of musically meaningful singing exercises, adapted to the user's level, will be shown. It will be based on the proper repetition of musical structures, while assuring the correctness of harmony and rhythm. Additionally, a module for singing assessment of the user's performance, in terms of intonation and rhythm, will be shown.

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Abbreviations

DTW:

dynamic time warping

EMI:

experiments in musical intelligence

IOI:

interonset interval

MIDI:

musical instrument digital interface

RMS:

root mean square

RSSM:

rhythm self-similarity matrix

SMO:

sequential minimal optimization

TIE:

total intonation error

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Acknowledgements

This work has been funded by Ministerio de Economía y Competitividad of the Spanish Government under Project No. TIN2016-75866-C3-2-R. This work has been done at Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech.

Author information

Authors and Affiliations

  1. Departamento de Ingeniería de Comunicaciones, ETSI Telecomunicación, Universidad de Málaga, Campus de Teatinos s/n, 29071, Malaga, Spain

    Lorenzo J. Tardón & Emilio Molina

  2. ATIC Research Group, Dep. Ingeniería de Comunicaciones, ETSI Telecomunicación, Universidad de Málaga, Campus de Teatinos s/n, 29071, Malaga, Spain

    Isabel Barbancho

  3. ATIC Research Group, Dep. Ingeniería de Comunicaciones, ETSI Telecomunicación, Universidad de Málaga, Campus de Teatinos s/n, 29071, Malaga, Spain

    Carles Roig

  4. ATIC Research Group, Dep. Ingeniería de Comunicaciones, ETSI Telecomunicación, Universidad de Málaga, Campus de Teatinos s/n, 29071, Malaga, Spain

    Ana M. Barbancho

Authors
  1. Lorenzo J. Tardón
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  2. Isabel Barbancho
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  3. Carles Roig
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  4. Emilio Molina
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  5. Ana M. Barbancho
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Corresponding author

Correspondence to Lorenzo J. Tardón .

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Editors and Affiliations

  1. Institute of Systematic Musicology, University of Hamburg, Neue Rabenstr. 13, 20354, Hamburg, Germany

    Rolf Bader

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Tardón, L.J., Barbancho, I., Roig, C., Molina, E., Barbancho, A.M. (2018). Music Learning: Automatic Music Composition and Singing Voice Assessment. In: Bader, R. (eds) Springer Handbook of Systematic Musicology. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-55004-5_42

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  • DOI: https://doi.org/10.1007/978-3-662-55004-5_42

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  • Online ISBN: 978-3-662-55004-5

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