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Modeling, Analyzing, Identifying, and Synthesizing Expressive Popular Music Performances

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Guide to Computing for Expressive Music Performance

Abstract

Professional musicians manipulate sound properties such as pitch, timing, amplitude, and timbre in order to add expression to their performances. However, there is little quantitative information about how and in which contexts this manipulation occurs. In this chapter, we describe an approach to quantitatively model and analyze expression in popular music monophonic performances, as well as identifying interpreters from their playing styles. The approach consists of (1) applying sound analysis techniques based on spectral models to real audio performances for extracting both inter-note and intra-note expressive features, and (2) based on these features, training computational models characterizing different aspects of expressive performance using machine learning techniques. The obtained models are applied to the analysis and synthesis of expressive performances as well as to automatic performer identification. We present results, which indicate that the features extracted contain sufficient information, and the explored machine learning methods are capable of learning patterns that characterize expressive music performance.

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Author information

Authors and Affiliations

  1. DTIC, Universitat Pompeu Fabra, Tànger, 122-140, 08018, Barcelona, Spain

    Rafael Ramirez

  2. CCRMA, Stanford University, 660 Lomita Dr, 94350, Stanford, CA, USA

    Esteban Maestre

  3. CIRMMT and IDMIL, Schulich School of Music, McGill University, 555 Sherbrooke St., West Montreal, Quebec, H3A 1E3, Canada

    Alfonso Perez

Authors
  1. Rafael Ramirez
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  2. Esteban Maestre
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  3. Alfonso Perez
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Corresponding author

Correspondence to Rafael Ramirez .

Editor information

Editors and Affiliations

  1. ICCMR, University of Plymouth, Smeaton Building 206, Plymouth, PL4 8AA, Devon, United Kingdom

    Alexis Kirke

  2. Computing, Communication & Electronics, University of Plymouth, Plymouth, PL4 8AA, United Kingdom

    Eduardo R. Miranda

Questions

Questions

  1. 1.

    What are the four musicological questions that this study attempts to answer?

  2. 2.

    Name three areas that could be helped by answers to these questions.

  3. 3.

    How is note segmentation done on the audio stream?

  4. 4.

    What are the two main principles recognized by Narmour in his theory?

  5. 5.

    How many prototypical Narmour structures are there?

  6. 6.

    How is bow direction detected in the gesture acquisition?

  7. 7.

    What levels of metrical strength are defined in the note descriptors?

  8. 8.

    What are some of the traditional deviations found in Irish jig music?

  9. 9.

    Why might the results be poor for the 1-note experiments?

  10. 10.

    What were the most successful and least successful classifiers in the results?

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© 2013 Springer-Verlag London

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Ramirez, R., Maestre, E., Perez, A. (2013). Modeling, Analyzing, Identifying, and Synthesizing Expressive Popular Music Performances. In: Kirke, A., Miranda, E. (eds) Guide to Computing for Expressive Music Performance. Springer, London. https://doi.org/10.1007/978-1-4471-4123-5_5

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  • DOI: https://doi.org/10.1007/978-1-4471-4123-5_5

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4122-8

  • Online ISBN: 978-1-4471-4123-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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