Slakh2100-16k for YourMT3

About this version:

This is a variant of Slakh2100 dataset (Manilow, 2019) resampled in '16 kHz-mono-16-bit-wav' format. We redistribute this data as a part of YourMT3 project. The license for redistribution is attached. Note that the 'omitted' directory of the slakh2100-redux version is reduced in this version. Thus, we have three splits: train, validation and test. 

MIRData integration:

Like the previous version of Slakh, this version will also be integrated into the MIRData (Bittner, 2019) project for convenient use. For this we provide an index file in 'json' format. The code for the customized MIRData is included in our YourMT3 project.

Citing YourMT3:

@misc{sungkyun_chang_2022_7470191,
  author = {Sungkyun Chang and Simon Dixon and Emmanouil Benetos},
  title = {{YourMT3: a toolkit for training multi-task and multi-track music transcription model for everyone}},
  month = dec,
  year = 2022,
  note = {{(Poster) Presented at DMRN+17: Digital Music Research Network One-day Workshop 2022}},
  publisher = {Zenodo},
  doi = {10.5281/zenodo.7470191},
  url = {https://doi.org/10.5281/zenodo.7470191}
}

About Slakh2100:

The Synthesized Lakh (Slakh) Dataset is a dataset of multi-track audio and aligned MIDI for music source separation and multi-instrument automatic transcription. Individual MIDI tracks are synthesized from the Lakh MIDI Dataset v0.1 using professional-grade sample-based virtual instruments, and the resulting audio is mixed together to make musical mixtures. This release of Slakh, called Slakh2100, contains 2100 automatically mixed tracks and accompanying, aligned MIDI files, synthesized from 187 instrument patches categorized into 34 classes, totaling 145 hours of mixture data.

Citing Slakh & MIRData:

@inproceedings{manilow2019cutting,
  title={Cutting Music Source Separation Some {Slakh}: A Dataset to Study the Impact of Training Data Quality and Quantity},
  author={Manilow, Ethan and Wichern, Gordon and Seetharaman, Prem and Le Roux, Jonathan},
  booktitle={Proc. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)},
  year={2019},
  organization={IEEE}
}

@inproceedings{
  bittner_fuentes_2019,
  title={mirdata: Software for Reproducible Usage of Datasets},
  author={Bittner, Rachel M and Fuentes, Magdalena and Rubinstein, David and Jansson, Andreas and Choi, Keunwoo and Kell, Thor},
  booktitle={International Society for Music Information Retrieval (ISMIR) Conference},
  year={2019}
}

Acknowledgement:

We thank the Zenodo team for allowing us additional storage.