IDEAS home Printed from https://ideas.repec.org/a/spr/advdac/v18y2024i3d10.1007_s11634-024-00594-6.html
   My bibliography  Save this article

Liszt’s Étude S.136 no.1: audio data analysis of two different piano recordings

Author

Listed:
  • Matteo Farnè

    (Alma Mater Studiorum Università di Bologna)

Abstract

In this paper, we review the main signal processing tools of Music Information Retrieval (MIR) from audio data, and we apply them to two recordings (by Leslie Howard and Thomas Rajna) of Franz Liszt’s Étude S.136 no.1, with the aim of uncovering the macro-formal structure and comparing the interpretative styles of the two performers. In particular, after a thorough spectrogram analysis, we perform a segmentation based on the degree of novelty, in the sense of spectral dissimilarity, calculated frame-by-frame via the cosine distance. We then compare the metrical, temporal and timbrical features of the two executions by MIR tools. Via this method, we are able to identify in a data-driven way the different moments of the piece according to their melodic and harmonic content, and to find out that Rajna’s execution is faster and less various, in terms of intensity and timbre, than Howard’s one. This enquiry represents a case study able to show the potentialities of MIR from audio data in supporting traditional music score analyses and in providing objective information for statistically founded musical execution analyses.

Suggested Citation

  • Matteo Farnè, 2024. "Liszt’s Étude S.136 no.1: audio data analysis of two different piano recordings," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 18(3), pages 797-822, September.
    • Handle: RePEc:spr:advdac:v:18:y:2024:i:3:d:10.1007_s11634-024-00594-6
    DOI: 10.1007/s11634-024-00594-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11634-024-00594-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11634-024-00594-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Patrick Georges & Ngoc Nguyen, 2019. "Visualizing music similarity: clustering and mapping 500 classical music composers," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 975-1003, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Patrick Georges & Aylin Seckin, 2022. "Music information visualization and classical composers discovery: an application of network graphs, multidimensional scaling, and support vector machines," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2277-2311, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:advdac:v:18:y:2024:i:3:d:10.1007_s11634-024-00594-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.