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Dilemmadata v1.0

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@johentsch johentsch released this 20 Apr 09:41
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v1.0
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This repository contains the processed and aligned data from two major annotated music corpora: the AugmentedNet dataset and the Distant Listening Corpus (DLC). The data has been preprocessed and converted into pitch arrays — tabular representations suitable for graph-based machine learning models used in automated music analysis tasks.

Overview

This project serves as the data infrastructure for training graph neural networks (GNNs) on multiple music analysis tasks, including:

  • Cadence detection (identifying cadence types in musical passages)
  • Phrase segmentation (marking phrase boundaries)
  • Key analysis (local and global key detection)
  • Harmonic analysis (chord quality, inversion, root, bass note)
  • Roman numeral analysis (functional harmonic analysis)
  • Rhythmic analysis (downbeat and metrical analysis)
  • Voice leading (analysis of voice leading patterns)
  • Section segmentation (identifying structural sections)
  • Pedal point detection (sustained bass notes)
  • Note degree inference (scale degrees relative to local key)

This resource has been demonstrated through the AnalysisGNN framework [Code][Paper] and serves as a foundation for training neural networks on automated music analysis tasks using multi-task learning and graph-based representations.

Data Sources

1. AugmentedNet Dataset

Source: github.com/napulen/AugmentedNet

AugmentedNet is an automatic Roman numeral analysis neural network developed by Néstor Nápoles López as part of his PhD research. The dataset includes:

  • 353 pieces from multiple collections (Beethoven Piano Sonatas, Bach chorales, TAVERN, etc.)
  • Roman numeral annotations for harmonic analysis
  • MusicXML scores with RomanText annotations
  • Split: Pre-defined test/training/validation splits (v1.0.0 dataset)

Key features:

  • Cadence annotations (cadential labels)
  • Roman numeral analysis (functional harmony)
  • Chord annotations with inversions
  • Synthetic training examples via texturization

Reference:

Nápoles López, N., Gotham, M., & Fujinaga, I. (2021). AugmentedNet: A Roman Numeral Analysis Network with Synthetic Training Examples and Additional Tonal Tasks. In Proceedings of the 22nd International Society for Music Information Retrieval Conference (pp. 404–411). https://doi.org/10.5281/zenodo.5624533

2. Distant Listening Corpus (DLC)

Source: github.com/DCMLab/distant_listening_corpus

The Distant Listening Corpus is a large-scale collection of annotated musical scores from the DCML (Digital and Cognitive Musicology Lab) corpus initiative. It includes over 40 subcorpora spanning music from the 17th to 20th centuries:

  • Bach, Beethoven, Chopin, Mozart, Schubert, etc.
  • Comprehensive harmonic annotations using the DCML standard
  • MuseScore 3.6.2 files with embedded annotations
  • TSV exports of notes, measures, chords, and harmony labels

Included subcorpora (selected):

  • beethoven_piano_sonatas, chopin_mazurkas, mozart_piano_sonatas
  • bach_en_fr_suites, bach_solo, schubert_winterreise
  • debussy_suite_bergamasque, grieg_lyric_pieces, liszt_pelerinage
  • monteverdi_madrigals, scarlatti_sonatas, wagner_overtures
  • And many more...

Key features:

  • Phrase boundaries
  • Cadence annotations
  • Local and global key annotations
  • Pedal point annotations
  • Section start markers
  • Note degree annotations (scale degree relative to local key)

Reference:

Hentschel, J., Rammos, Y., Neuwirth, M., & Rohrmeier, M. (2025). A corpus and a modular infrastructure for the empirical study of (an)notated music. Scientific Data, 12(1), 685. https://doi.org/10.1038/s41597-025-04976-z

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