DOI: 10.5281/zenodo.15003027
title: Claudio Monteverdi – Madrigals (A corpus of annotated scores)

description: This is a README file for a data repository originating from the DCML corpus initiative
and serves as welcome page for both



the GitHub repo https://github.com/DCMLab/monteverdi_madrigals and the corresponding

documentation page https://dcmlab.github.io/monteverdi_madrigals


For information on how to obtain and use the dataset, please refer to this documentation page.

Claudio Monteverdi – Madrigals (A corpus of annotated scores)

This corpus of annotated MuseScore files has been created within
the DCML corpus initiative and employs
the\r DCML harmony annotation standard.
Claudio Monteverdi's nine books of madrigals span the entirety of the composer's\r
career, from his adolescence to his very old age. For this reason, they evince the transition, Monteverdi's\r
great innovation, from the blended interlocking lines of Renaissance polyphony to the solid verticals and\r
speech-like rhythms of Baroque operatic homophony. Arguably the centrepiece of this collection is the\r
ubiquitous and sensational "Cruda Amarilli," whose striking unprepared dissonances scandalized fellow\r
composer Giovanni Artusi and prompted an infamous feud between the two musicians. Besides a selection\r
of madrigals, this repository also features the stunning and energetic "Laudate pueri," a posthumously\r
published psalm setting that merges rich Renaissance-style imitation with definitively Monteverdian\r
text-setting techniques.

Getting the data



download repository as a ZIP file

download a Frictionless Datapackage that includes concatenations
of the TSV files in the four folders (measures, notes, chords, and harmonies) and a JSON descriptor:


monteverdi_madrigals.zip

monteverdi_madrigals.datapackage.json



clone the repo: git clone https://github.com/DCMLab/monteverdi_madrigals.git


Data Formats

Each piece in this corpus is represented by five files with identical name prefixes, each in its own folder.
For example, the 12th madrigal from the 2nd book has the following files:



MS3/2-12.mscx: Uncompressed MuseScore 3.6.2 file including the music and annotation labels.

notes/2-12.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/2-12.measures.tsv: A table with relevant information about the measures in the score.

chords/2-12.chords.tsv: A table containing layer-wise unique onset positions with the musical markup (such as dynamics, articulation, lyrics, figured bass, etc.).

harmonies/2-12.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 MS3 with the free and open source score
editor 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/CSV or the free alternative
LibreOffice 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:

import ms3

labels = ms3.load_tsv("harmonies/2-12.harmonies.tsv")
notes = ms3.load_tsv("notes/2-12.notes.tsv")


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.

Cite as



Johannes Hentschel, Yannis Rammos, Markus Neuwirth, & Martin Rohrmeier. (2025). Claudio Monteverdi – Madrigals (A corpus of annotated scores) [Data set]. Zenodo. https://doi.org/{{ concept_doi }}


License

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).