Corrections & metadata update #63
Merged
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This is a README file for a data repository originating from the DCML corpus initiative
and serves as welcome page for both
For information on how to obtain and use the dataset, please refer to this documentation page.
The Annotated Beethoven Corpus (ABC) (A corpus of annotated scores)
The ABC dataset consists of expert harmonic analyses of all Beethoven string quartets
(opp. 18, 59, 74, 95, 127, 130, 131, 132, 135, composed between 1800 and 1826), encoded in a human- and
machine-readable format (MuseScore format).
Using a modified Roman Numeral notation (the DCML harmony annotation standard),
the dataset includes the common music-theoretical set of harmonic features such as key, chordal root,
chord inversion, chord extensions, suspensions, and others.
Getting the data
of the TSV files in the four folders (
measures,notes,chords, andharmonies) and a JSON descriptor:git clone https://github.com/DCMLab/ABC.gitData Formats
Each piece in this corpus is represented by five files with identical name prefixes, each in its own folder.
For example, the first movement of the first quartet, op. 18/1, has the following files:
MS3/n01op18-1_01.mscx: Uncompressed MuseScore 3.6.2 file including the music and annotation labels.notes/n01op18-1_01.notes.tsv: A table of all note heads contained in the score and their relevant features (not each of them represents an onset, some are tied together)measures/n01op18-1_01.measures.tsv: A table with relevant information about the measures in the score.chords/n01op18-1_01.chords.tsv: A table containing layer-wise unique onset positions with the musical markup (such as dynamics, articulation, lyrics, figured bass, etc.).harmonies/n01op18-1_01.harmonies.tsv: A table of the included harmony labels (including cadences and phrases) with their positions in the score.Each TSV file comes with its own JSON descriptor that describes the meanings and datatypes of the columns ("fields") it contains,
follows the Frictionless specification,
and can be used to validate and correctly load the described file.
Opening Scores
After navigating to your local copy, you can open the scores in the folder
MS3with the free and open source scoreeditor MuseScore. Please note that the scores have been edited, annotated and tested with
MuseScore 3.6.2.
MuseScore 4 has since been released which renders them correctly but cannot store them back in the same format.
Opening TSV files in a spreadsheet
Tab-separated value (TSV) files are like Comma-separated value (CSV) files and can be opened with most modern text
editors. However, for correctly displaying the columns, you might want to use a spreadsheet or an addon for your
favourite text editor. When you use a spreadsheet such as Excel, it might annoy you by interpreting fractions as
dates. This can be circumvented by using
Data --> From Text/CSVor the free alternativeLibreOffice Calc. Other than that, TSV data can be loaded with
every modern programming language.
Loading TSV files in Python
Since the TSV files contain null values, lists, fractions, and numbers that are to be treated as strings, you may want
to use this code to load any TSV files related to this repository (provided you're doing it in Python). After a quick
pip install -U ms3(requires Python 3.10 or later) you'll be able to load any TSV like this:Version history
See the GitHub releases.
Questions, Suggestions, Corrections, Bug Reports
Please create an issue and/or feel free to fork and submit pull requests.
Publications
Cite as
License
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).
fixes #29