Published November 1, 2018 | Version 1.0
Dataset Restricted

Enhanced Wikifonia Leadsheet Dataset

Creators

  • 1. Università di Milano

Description

EWLD (Enhanced Wikifonia Leadsheet Dataset) is a music lead sheet dataset with more than 5.000 scores that comes with a lot of metadata about composers, works, lyrics, and features. It is designed for musicological and research purposes.

A Public Domain version, named OpenEWLD, is available at https://github.com/00sapo/OpenEWLD.

You can find an in-deep discussion in my Master Thesis.

Request access through Zenodo (click on the "request access" button below) and provide the following information:

  1. Name and Surname(s)
  2. Institution
  3. Role in the institution
  4. The sentence "The data will never be used for commercial and/or profit purposes by me or other people I will give the data to."
  5. One line to explain the research project

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Please, use the following paper as reference:

Simonetta, Federico, Carnovalini, Filippo, Orio, Nicola, & Rodà, Antonio. (2018). Symbolic Music Similarity Through a Graph-Based Representation. In Proceedings of the Audio Mostly 2018 on Sound in Immersion and Emotion (pp. 26:1–26:7). New York, NY, USA: ACM. http://doi.org/10.1145/3243274.3243301

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For further info, see my MS Thesis:

F. Simonetta, “Graph based representation of the music symbolic level. A music information retrieval application”, Università di Padova, 2018.

Zenodo link: https://zenodo.org/record/1476564

 

Files

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Request access

If you would like to request access to these files, please fill out the form below.

You need to satisfy these conditions in order for this request to be accepted:

You should have a proved research aim and you should provide proofs that you will not redistribuitethe dataset freely.

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Additional details

References

  • Simonetta, Federico, Carnovalini, Filippo, Orio, Nicola, & Rodà, Antonio. (2018). Symbolic Music Similarity Through a Graph-Based Representation. In Proceedings of the Audio Mostly 2018 on Sound in Immersion and Emotion (pp. 26:1–26:7). New York, NY, USA: ACM. http://doi.org/10.1145/3243274.3243301