[Dataset] EnsembleSet: A new high-quality synthesised dataset for chamber ensemble separation

[Saurjya Sarkar]

Dear all,

We are happy to announce the release of EnsembleSet: A new high-quality synthesized dataset for chamber ensemble separation. EnsembleSet was generated using the Spitfire BBC Symphony Orchestra library and ensemble scores from RWC Classical Music Database and Mutopia.

Our data generation method introduces automated articulation mapping based on the input MIDI/MusicXML data for different playing styles. The sample library also enables us to render the dataset with 20 different mix/microphone configurations (500 hours of rendered instruments) allowing us to study various recording scenarios for each performance. The dataset presents 80 tracks (6+ hours) with a range of string, wind, and brass instruments arranged as chamber ensembles.

The presented dataset not only contains high-quality multi-microphone renders of various instruments but is also accompanied by the MIDI files that were utilized for generating this dataset. This paired data can be utilized for various tasks including multi-instrument transcription, instrument recognition, score-informed source separation, microphone simulation, and automatic mixing.

Dataset Contents:
 
80 Chamber ensemble pieces rendered from MIDI/MusicXML files using Spitfire BBC Symphony Orchestra Professional Library. Each folder contains 20 sub-folders containing 20 unique microphone/mix configurations along with MIDI files.
 
The dataset: https://zenodo.org/record/7327175
The corresponding paper:  https://archives.ismir.net/ismir2022/paper/000075.pdf
ISMIR 2022 Poster/Video: https://ismir2022program.ismir.net/poster_277.html

If you have any questions about EnsembleSet, please feel free to contact us.

Regards,
Saurjya Sarkar, Emmanouil Benetos, Mark Sandler

Queen Mary University of London

Mile End Road, London, E1 4NS

United Kingdom

Mobile: +44 7923 358211