CocoChorales
dataset·synthetic multitrack music·active
Synthetic four-part chamber-ensemble music with mixtures, stems, MIDI and fine-grained performance annotations.
Verified facts
| Official site | https://magenta.tensorflow.org/ceg-and-cocochorales ↗ |
|---|---|
| Geography | United States |
| size | pieces: 240000 · instruments: 13 · mixture hours: 1411 · ensemble types: 4 |
| scope | Generated Bach-style four-part chamber ensembles across string, brass, woodwind and random instrumentations. |
| genres | Bach chorales; chamber ensemble |
| steward | Google Magenta |
| creators | Yusong Wu; Josh Gardner; Ethan Manilow; Ian Simon; Curtis Hawthorne; Jesse Engel |
| languages | not_applicable |
| modalities | audio mixtures; audio stems; MIDI; multi-f0; performance annotations; synthesis parameters |
| geographies | Global |
| access method | Official project page and GitHub download instructions. |
| intended uses | transcription; source separation; multi-f0 estimation; performance analysis |
| related papers | The Chamber Ensemble Generator: Limitless High-Quality MIR Data via Generative Modeling |
| annotation method | Structured generators emit source, note, expression and synthesis ground truth. |
| collection method | Generated by the Chamber Ensemble Generator using Coconet and MIDI-DDSP. |
| provenance claims | Coconet used 382 Bach chorales; MIDI-DDSP used 44 URMP examples before amplification. |
| current availability | Download instructions linked from official project page. |
| us market scope basis | Publicly available or materially used in US-facing music/audio-AI research. |
| limitations biases disputes | Entire corpus is synthetic and stylistically concentrated on Bach chorales. |
| related models tools benchmarks | Coconet; MIDI-DDSP; URMP |
Current
| generated using training data | URMP Dataset |
|---|
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