Datasets
Participants are welcome to train their systems on any dataset, including publicly available corpora, proprietary collections, or internally curated material. There are no restrictions on dataset origin, but we ask for full transparency.
Please clearly describe the datasets used for training and validation in your technical report. Important details to include are:
- Dataset name or source
- Size and number of pieces
- Instrumentation and expressive characteristics
- Data format (MIDI, audio, etc.)
- Any preprocessing, cleaning, or augmentation steps applied
This helps the jury and the research community understand the representational capacity and limitations of each submission.
Post-Processing
To ensure fair evaluation, all post-processing applied to the preliminary round output must be documented in the submission report. Depending on your system type, please include the following:
- Symbolic-to-Audio Systems: If your model generates symbolic output (e.g., MIDI), describe how audio is rendered. Include soundfont names, software synths used (e.g., FluidSynth, Logic Pro), or player piano models.
- Please contact huan.zhang@qmul.ac.uk if you want to play and record your symbolic output in the Disklavier of Queen Mary University of London.
- Direct Audio Systems: If your model outputs audio directly, describe any enhancement steps such as EQ, reverb, compression, or noise reduction.
- Controllability or Interventions: Clarify if the output is influenced by human-involved choices — such as selected tempo, dynamics range, segmentation, or annotated phrasing.
- MIDI cleanup: If symbolic outputs were manually edited (quantization, pedals, etc) before submission, that should be documented.
Submissions should aim for minimal human intervention. Manual correction is allowed only if it is well-documented and justified in the report.