Published January 19, 2025
| Version v1
Dataset
Open
BFDB: A dataset of British Folk melodies in ABC Format.
-
1.
Queen Mary University of London
- 2. Aix-Marseille Univ CNRS PRISM
Description
The British Folk Database (BFDB) is a collection of 13,835 dated folk tunes (monophonic melodies) in 'ABC' format, a text-based music notation system widely used for folk music.
These tunes were collected from online resources of ABC notation. Each tune comes with a corresponding date of collection, which is the year that the tune was originally collected and/or published. Most of these tunes come from 'tune books' which were subsequently digitised into ABC format.
This dataset may be helpful for computational musicology tasks, MIR and music evolution analysis.
File Structure
1. tune_abc_files/
- This folder contains all the folk tunes in ABC format.
- Each file is named using the pattern:
yearOfCollection_Title.abc
Example:1740_Welch March.abc.
2. dataset.csv
- This file provides metadata for each tune:
- filename: Filename of each tune
- year: Year that the tune was originally collected
- title: Title of each tune
- source: URL to where the tune was found online and where applicable, the name of the tune book that it comes from
- composer/performer: Composer/Perfomer/Collector of the tune
- metre: Metre of the tune
- key: Musical key of the tune
- tempo: Suggested performance speed
- style: Genre or style of the tune (e.g., jig, reel, march)
- digital_transcriber: Name of the person who transcribed the tune in ABC format
- If any of these details are unavailable, the value is marked as
N/A.
3. tune_pitch_rhythm_vectors.tsv
- This file contains pitch and rhythm vectors extracted from each tune.
- Pitch Vectors: Represent the relative intervals between successive notes.
- Rhythm Vectors: Represent the relative note values, e.g. in 4/4 time:
- A quarter note =
1.0 - A semi-quaver =
0.5
- A quarter note =
Files
dataset.csv
Additional details
Identifiers
Related works
- Continues
- Dataset: https://github.com/Gubbledenut/ABC_TuneBooks (URL)
- Dataset: http://www.cpartington.plus.com/Links/ChrisPartingtonsLinksPage.html#THE_COLLECTIONS (URL)
Dates
- Submitted
-
2025-01-23
References
- @article{savage_sequence_2022, title = {Sequence alignment of folk song melodies reveals cross-cultural regularities of musical evolution}, volume = {32}, issn = {09609822}, url = {\url{https://linkinghub.elsevier.com/retrieve/pii/S0960982222000926}, doi = {10.1016/j.cub.2022.01.039}}, language = {en}, number = {6}, urldate = {2023-11-26}, journal = {Current Biology}, author = {Savage, Patrick E. and Passmore, Sam and Chiba, Gakuto and Currie, Thomas E. and Suzuki, Haruo and Atkinson, Quentin D.}, month = mar, year = {2022}, pages = {1395--1402.e8}, file = {Savage et al. - 2022 - Sequence alignment of folk song melodies reveals c.pdf:/Users/adamdeedman/Zotero/storage/PK43MD25/Savage et al. - 2022 - Sequence alignment of folk song melodies reveals c.pdf:application/pdf}, }
- @inproceedings{walshaw_constructing_2016, address = {Dublin}, title = {Constructing {Proximity} {Graphs} to {Explore} {Similarities} in {Large}-{Scale} {Melodic} {Datasets}}, language = {en}, booktitle = {6th {International} {Workshop} in {Folk} {Music} {Analysis}}, author = {Walshaw, Chris}, year = {2016}, file = {Walshaw - 2016 - Constructing Proximity Graphs to Explore Similarit.pdf:/Users/adamdeedman/Zotero/storage/YDEX5YMK/Walshaw - 2016 - Constructing Proximity Graphs to Explore Similarit.pdf:application/pdf}, }
- @inproceedings{walshaw_visualising_2018, address = {Thessaloniki}, title = {Visualising {Melodic} {Similarities} in {Folk} {Music}}, url = {\url{https://scholar.googleusercontent.com/scholar?q=cache:KLqq9-KGFCUJ:scholar.google.com/&hl=en&as_sdt=0,5}}, urldate = {2024-08-19}, booktitle = {8th {International} {Workshop} on {Folk} {Music} {Analysis}}, author = {Walshaw, Chris}, year = {2018}, }
- @article{metzig_classification_2020, title = {Classification of origin with feature selection and network construction for folk tunes}, volume = {133}, issn = {0167-8655}, url = {\url{https://www.sciencedirect.com/science/article/pii/S016786552030101X}}, doi = {10.1016/j.patrec.2020.03.023}, urldate = {2024-06-07}, journal = {Pattern Recognition Letters}, author = {Metzig, Cornelia and Gould, Matthew and Noronha, Roshan and Abbey, Roshani and Sandler, Mark and Colijn, Caroline}, month = may, year = {2020}, keywords = {Melody feature extraction, Random forest, Similarity networks}, pages = {356--364}, file = {ScienceDirect Snapshot:/Users/adamdeedman/Zotero/storage/59Q5WYRP/S016786552030101X.html:text/html}, }
- @incollection{sharp_evolution_1907, title = {Evolution}, url = {\url{http://archive.org/details/englishfolksongs00shar}}, language = {eng}, urldate = {2024-07-02}, booktitle = {English folk song, some conclusions}, publisher = {London : Simpkin \& co., ltd.}, author = {Sharp, Cecil James}, collaborator = {{University of California Libraries}}, year = {1907}, keywords = {Folk songs, English -- History and criticism}, pages = {16 -- 31}, }
- @inproceedings{volk_unfolding_2011, address = {Fryske Akademy}, title = {Unfolding the potential of computational musicology}, abstract = {This paper addresses current chances and challenges in computational musicology. Computational musicology is a genuinely interdisciplinary research area that requires the contribution of questions, methods and insights from both musicology and computer science. This paper demonstrates ho{\textasciitilde} computational approaches to musicological questions generate new perspectives for musicology. In turn, computational musicology has the potential to become an indispensible partner for computer science in Mus{\textasciitilde}c Inf{\textasciitilde}{\textasciitilde}at{\textasciitilde}on Retrieval. We argue that, for unfolding the potential of computational musicology, the full mterdlsclplmary enterprise has yet to be realized and we discuss examples of promising collaborative directions.}, language = {en}, booktitle = {Proceedings of the 13th {International} {Conference} on {Informatics} and {Semiotics} in {Organisations}}, author = {Volk, Anja and Wiering, Frans and van Kranenburg, Peter}, year = {2011}, pages = {137--144}, file = {PDF:/Users/adamdeedman/Zotero/storage/PN4JSL8S/van Kranenburg - Unfolding the potential of computational musicology.pdf:application/pdf}, }