Repository in support to MIDI2vec: Learning MIDI Embeddings for Reliable Prediction of Symbolic Music Metadata.

Pasquale Lisena, Albert Meroño-Peñuela, Raphaël Troncy. MIDI2vec: Learning MIDI Embeddings for Reliable Prediction of Symbolic Music Metadata, to appear in Semantic Web Journal, Special Issue on Deep Learning for Knowledge Graphs, 2021. http://www.semantic-web-journal.net/content/midi2vec-learning-midi-embeddings-reliable-prediction-symbolic-music-metadata-0

The experiment is available under 3 notebooks, covering 3 datasets:

• SLAC http://jmir.sourceforge.net/Codaich.html - direct download
• Musedata http://old.musedata.org/
• Lakh https://colinraffel.com/projects/lmd/

The MIDI2vec library is available here.

Pre-computed MIDI embeddings used in the paper are available in Zenodo.

Process to be performed for each <dataset_folder>.

• Clone midi2vec repository and enter in it
• Generate edgelists (absolute paths to prefer)

cd midi2edgelist
npm install
node index.js -i <dataset_folder>
node index.js -i <dataset_folder> -o edgelist_300 -n 300

• Compute embeddings

cd ../
pip install -r edgelist2vec/requirements.txt

python edgelist2vec/embed.py -o embeddings/<dataset>.bin
python edgelist2vec/embed.py -o embeddings/<dataset>_notes.bin --exclude notes
python edgelist2vec/embed.py -o embeddings/<dataset>_program.bin --exclude program
python edgelist2vec/embed.py -o embeddings/<dataset>_tempo.bin --exclude tempo
python edgelist2vec/embed.py -o embeddings/<dataset>_timesig.bin --exclude time.signature
python edgelist2vec/embed.py -i edgelist_300 -o embeddings/<dataset>_300.bin

• For SLAC and Musedata's CCV experimet, we use the script in split_datasets for splitting them in 10 folds Then, for each fold i, the following commands need to be run

node index.js -i <dataset_splitted_folder>/fold<i>/train -o edgelist<i>
node index.js -i <dataset_splitted_folder>/fold<i>/test -o edgelist<i>_test

python edgelist2vec/embed.py -i edgelist<i> -o <dataset><i>.bin

• Copy the generated embedding files and edgelists (eventually from the pretrained zip files) in the relative dataset folder (together with the .csv) of midi-embs (this project)
• Open the following and run it:
  • SLAC: classification-slac.ipynb
  • Musedata: classification-musedata.ipynb
  • Lakh: classification-lakh.ipynb