@inproceedings{hawthorne2018enabling,
title= {Enabling Factorized Piano Music Modeling and Generation with the {MAESTRO} Dataset},
author= {Curtis Hawthorne and Andriy Stasyuk and Adam Roberts and Ian Simon and Cheng-Zhi Anna Huang and Sander Dieleman and Erich Elsen and Jesse Engel and Douglas Eck},
booktitle= {International Conference on Learning Representations},
year= {2019},
url= {https://magenta.tensorflow.org/datasets/maestro},
abstract= {MAESTRO (MIDI and Audio Edited for Synchronous TRacks and Organization) is a dataset composed of over 200 hours of virtuosic piano performances captured with fine alignment (~3 ms) between note labels and audio waveforms.},
keywords= {Audio Midi MIDI Piano Music},
terms= {},
license= {},
superseded= {}
}