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---
license: cc-by-sa-4.0
dataset_info:
 features:
 - name: title
 dtype: string
 - name: artist
 dtype: string
 - name: track_id
 dtype: string
 - name: year
 dtype: int32
 - name: song_id
 dtype: string
 - name: chords
 sequence:
 - name: symbol
 dtype: string
 - name: timestamp
 dtype: float32
 splits:
 - name: train
 num_bytes: 29227429
 num_examples: 31032
 download_size: 8563052
 dataset_size: 29227429
configs:
- config_name: default
 data_files:
 - split: train
 path: data/train-*
---
# Dataset Card - Chords from the Lakh MIDI Dataset
## Dataset Summary
The Chords from the Lakh MIDI Dataset (LMD) is a collection of 31032 chord sequences extracted from selected MIDI files of the [Lakh MIDI Dataset](https://colinraffel.com/projects/lmd/)
extracted using the Python library [chord-extractor](https://github.com/ohollo/chord-extractor), which has the ability to take MIDI, MP3, WAV and other
sound files in bulk, and extract chords using the [Chordino](https://code.soundsoftware.ac.uk/projects/nnls-chroma/) method.
The music files chosen are those that the LMD Dataset curator was able to best match to songs in the
[Million Song Dataset](http://millionsongdataset.com/). In doing so we are able to include here the song title, artist and other
metadata associated with the chord sequences.
## Dataset Structure
### Data Instances
A typical data point comprises the chord sequences (with when they occur in the song), IDs so that one can source the metadata or
actual MIDI files themselves, along with some information about the original piece of music that the MIDI is based on.
An example from the dataset looks as follows:
```
{
 'title': "Movin' Out (Anthony's Song)",
 'artist': 'Billy Joel',
 'track_id': 'TRNCSKU128F4265639',
 'year': 1977,
 'song_id': 'SOIJWHG12A8C134063',
 'chords': {
 'symbol': ['N', Dm', 'Bb', 'C','Eaug', 'Am', 'Dm', 'Gm7', 'C', 'Eaug', 'Am', ...],
 'timestamp': [0.371519274, 0.4643990993499756, 1.8575963973999023, 3.7151927947998047, 4.551111221313477, ...]
 }
}
```
### Data Fields
- `title`: The title of the song whose chords have been interpreted.
- `artist`: Artist/performer of the song.
- `track_id`: The track ID as used by Echo Nest, a source of data for the Million Song Dataset. This is also important as
it is used in LMD's "LMD-matched" dataset as the directory names containing the MIDIs corresponding to a particular song from the MSD.
 Therefore use this to navigate those files.
- `year`: Year that the song was first released (see considerations) if provided. If not provided by MSD, this
is denoted 0. Of 31032 total records, 16832 have years.
- `song_id`: "Song ID" as provided by the Million Song Dataset.
- `chords`: Two lists of same length describing chord progressions in song
 - `symbol`: List of chord symbols in the order as they appear in the song.
The list is bookended by Chordino with two dummy 'N' symbols to assist with when the track starts and ends.
 - `timestamp`: Timestamps (in seconds) at which the chords appear in the song. The index of the timestamp,
corresponds to the chord symbol in the same index as "symbol".
### Data Splits
The data currently has one split - "train".
## Dataset Creation
### Curation Rationale
The LMD Chords are created to provide a chord sequence dataset of reasonable size such that potentially good insights
can be made into harmonic progression. Enhanced analysis is also possible with the timestamps of these chords provided.
The selection of files matched with MSD should give a decent representation of different genres, years, artists etc.
With the lack of accessible large publicly available datasets with the actual original songs, for example in MP3 format,
MIDIs are seen as a viable alternative, as they should relay the notes played in the song, despite not being timbrally
the same as the original.
Furthermore, the IDs of the Million Song Dataset provided give the user the opportunity to map the chords to the wealth of audio
analysis held in the full version of the MSD. These not only include an expanded version of the metadata provided here,
but features such as tempo, key, loudness and much more.
### Source Data
#### Initial Data Collection and Selection
To source the music files used for chord extraction, three files are needed for download:
- The set of MIDI files that have been matched to the Million Song Dataset, called "LMD-matched" available
on the [LMD website](https://colinraffel.com/projects/lmd/).
- On the same page, are the Match Scores, that show the match confidence score between each MIDI file and
the associated MSD record. This JSON file contains a map between MSD/Echo Nest track IDs and the associated MIDI filenames
in LMD and their score.
- The Summary File of the whole Million Song Dataset, available on [this page](http://millionsongdataset.com/pages/getting-dataset/).
Note this is a 300MB HDF5 with just the metadata. The whole MSD (which is 280GB) is not needed to create this dataset.
The LMD Chords dataset only uses one MIDI per song. The Match Scores file was used to see the
best matching MIDI to a particular song from MSD, and that file was then included here.
The name of a directory holding a particular MIDI is the same as the track ID for that song used by MSD, and therefore was used
to map a MIDI file to the MSD and source its metadata.
#### Chord Extraction Process
The selected MIDI files are then passed to the [chord-extractor](https://github.com/ohollo/chord-extractor) v0.1.2 (full disclosure:
this library is also written by the creator of this dataset). The Chordino method is utilised, with an override on the spectral roll on parameter
set to 1 as below (note, the extract_many method automatically converts MIDIs to WAV prior to extraction).
```python
midi_paths = [...] # List of strings denoting paths to LMD MIDIs matched to MSD
extractor = Chordino(rollon=1)
res = extractor.extract_many(midi_paths) # Result provided to symbol and timestamp fields in dataset
```
Please refer to [this website](https://code.soundsoftware.ac.uk/projects/nnls-chroma/) to read more information about Chordino and the NNLS Chroma technique it uses.
## Considerations for Using the Data
### Limitations of the Chord Extractions
The chords listed here are interpretations made by the Chordino method, an algorithm applied to sound files, and as such
will likely deviate from official transcriptions and scores. The chord symbols effectively provide root notes, differentiations
between major and minor chords, major and dominant 7ths, as well as providing slash chords, and some others, the chords
types available are not exhaustive. It is not able for example to identify complex jazz chords such as Fmaj7#11.
It should also be noted that the MIDIs were converted to WAV sound files prior to extraction (as this was the format
needed by chord-extractor). In that way some information may be lost, compared to directly gleaning chords from
reading the note information available in the MIDI file.
Nevertheless, the extractions are of sufficient quality to give a good semblance of the "ground truth", particularly for
harmonically non-complex songs, which form the majority. The user is encouraged to compare some of the extractions here to their expectations.
### Accuracy of the Metadata
It should be noted that though the Million Song Dataset has sourced its metadata from The Echo Nest, it
is not immune from the occasional typo, though this is observed to be very rare with regard to song name and artist.
Other metadata in MSD can be open to interpretation (e.g. genre).
The user should familiarise themselves with how MSD has populated a field, if heavily relying on its data.
The year field as relayed in this dataset is a good example of this. It is only present for approximately half of the data points,
and though it is assumed to show the original year of the song's release, it should be verified that this is always the case.
### Legal and Ethical Considerations
This dataset contains interpretations of chord progressions from musical works. While chord progressions themselves are not
typically protected under copyright law (and the author is not aware of any infringement here), the original works they are derived from remain copyrighted. Users are
encouraged to respect the rights of the original artists when using this dataset.
This dataset is provided for research and educational purposes. Users should ensure their use of the dataset
complies with applicable copyright laws and licensing agreements in their jurisdiction.
## Additional Information
### Dataset Curators
The dataset was collated by Oliver Holloway.
### Licensing Information
The dataset is licensed under CC BY-SA 4.0. Please review the license for detailed usage terms.
### Citation Information
If using this dataset, please cite it by getting the latest Bibtex from the "Cite this dataset" button on this page.
It should resemble the following:
```
@misc {oliver_holloway_2025,
 author = { {Oliver Holloway} },
 title = { lmd_chords (Revision 4d6815c) },
 year = 2025,
 url = { https://huggingface.co/datasets/ohollo/lmd_chords },
 doi = { 10.57967/hf/4219 },
 publisher = { Hugging Face }
}
```
It would be great to mention the [chord-extractor Python library](https://github.com/ohollo/chord-extractor) as well.
Furthermore, this dataset is derived from the [Lakh MIDI Dataset](https://colinraffel.com/projects/lmd/) so please cite the below:
>Colin Raffel. **"Learning-Based Methods for Comparing Sequences, with Applications to Audio-to-MIDI Alignment and Matching"**. _PhD Thesis_, 2016.
If you use the metadata of this dataset, it is from the [Million Song Dataset](http://millionsongdataset.com/), so please reference this paper:
>Thierry Bertin-Mahieux, Daniel P. W. Ellis, Brian Whitman, and Paul Lamere. **"The Million Song Dataset"**. In _Proceedings of the 12th International Society for Music Information Retrieval Conference_, pages 591–596, 2011
Finally, the chords have been derived using the [Chordino](https://code.soundsoftware.ac.uk/projects/nnls-chroma/) method so please cite:
>M. Mauch and S. Dixon, **“Approximate Note Transcription for the Improved Identification of Difficult Chords,”** in _Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR 2010)_, 2010.