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Dataset Open Access

Schubert Winterreise Dataset

Christof Weiß; Frank Zalkow; Vlora Arifi-Müller; Meinard Müller; Hendrik Vincent Koops; Anja Volk; Harald G. Grohganz

The Schubert Winterreise Dataset (SWD) is a multimodal dataset comprising various representations and annotations of Franz Schubert's 24-song cycle Winterreise. The primary material (raw data) consists of textual representations of the songs' lyrics, music scores in image, symbolic, and MIDI format, as well as nine audio recordings of performances (only two included due to copyright issues). The secondary material (annotations) comprises information of musical measure positions in sheet music images and audio recordings as well as analyses of chords, local keys, global keys, and structural parts. The SWD is organized as follows:

01_RawData

  • lyrics_txt
  • score-IMSLP_png
  • score-IMSLP_pdf-complete.pdf
  • score_sibelius
  • score_pdf
  • score_musicxml
  • score_midi
  • audio_wav

02_Annotations

  • ann_score-IMSLP_measure
  • ann_score_chord
  • ann_score_localkey-ann1
  • ann_score_localkey-ann2
  • ann_score_localkey-ann3
  • ann_score_globalkey.csv
  • ann_score_structure
  • ann_audio_measure
  • ann_audio_chord
  • ann_audio_localkey-ann1
  • ann_audio_localkey-ann2
  • ann_audio_localkey-ann3
  • ann_audio_globalkey.csv
  • ann_audio_structure

03_ExtraMaterial

  • original data, scripts for cutting, licenses, ...

For a detailed description, please see the accompanying journal paper: Christof Weiß, Frank Zalkow, Vlora Arifi-Müller, Meinard Müller, Hendrik Vincent Koops, Anja Volk, and Harald G. Grohganz. Schubert Winterreise Dataset: A Multimodal Scenario for Music Analysis. In: ACM Journal on Computing and Cultural Heritage, XX:YY, 2020, under revision
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  • Frank Zalkow, Angel Villar Corrales, TJ Tsai, Vlora Arifi-Müller, and Meinard Müller. Tools for Semi-Automatic Bounding Box Annotation of Musical Measures in Sheet Music. In Demos and Late Breaking News of the International Society for Music Information Retrieval Conference (ISMIR). Delft, The Netherlands, 2019

  • Frans Absil. Musical Analysis – Visiting the Great Composers (6th ed.), 2017

  • Harald Grohganz. Algorithmen zur strukturellen Analyse von Musikaufnahmen. Ph.D. Dissertation. University of Bonn, Germany, 2015

  • Hendrik Vincent Koops. Computational Modelling of Variance in Musical Harmony. Ph.D. Dissertation. Utrecht University, Utrecht, The Netherlands, 2019

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