Terms of Use and Licensing Information
Dataset compiled by Rachel Bittner, Justin Salamon, Mike Tierney, Matthias Mauch, Chris Cannam, and Juan P. Bello. MedleyDB is offered free of charge for non-commercial research use only under the terms of the Creative Commons Attribution Noncommercial License. The dataset and its contents are made available on an "as is" basis and without warranties of any kind, including without limitation satisfactory quality and conformity, merchantability, fitness for a particular purpose, accuracy or completeness, or absence of errors. Subject to any liability that may not be excluded or limited by law, NYU is not liable for, and expressly excludes, all liability for loss or damage however and whenever caused to anyone by any use of the MedleyDB dataset or any part of it.
The audio provided by the artists was modified from its original version to suit the purposes of this dataset. These modifications include editing of the original timing, creating new mixes, adding new audio content, and/or recombining stems.
The audio provided by the artists was modified from its original version to suit the purposes of this dataset. These modifications include editing of the original timing, creating new mixes, adding new audio content, and/or recombining stems.
MedleyDB by R. Bittner et. al. is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Based on a work at http://marl.smusic.nyu.edu/medleydb.
We chose to release MedleyDB under a creative commons license to allow researchers to work with the dataset without restrictive legal constraints. However, while redistribution of the data is technically allowed under this license, we ask that you do not republish the dataset in full or in part without our consent. If you have any questions, please contact Rachel Bittner at rachel (dot) bittner (at) nyu (dot) edu.
Download MedleyDB
A sample version of MedleyDB can be downloaded here.
To download MedleyDB and MedleyDB 2.0 in their entirety, please request access via Zenodo at the following links. We will do our best to reply to requests promptly.
Please acknowledge our paper in academic research:
MedleyDB Original:
R. Bittner, J. Salamon, M. Tierney, M. Mauch, C. Cannam and J. P. Bello, "MedleyDB: A Multitrack Dataset for Annotation-Intensive MIR Research", in 15th International Society for Music Information Retrieval Conference, Taipei, Taiwan, Oct. 2014.
MedleyDB 2.0
Bittner, R., Wilkins, J., Yip, H., & Bello, J. (2016). MedleyDB 2.0: New Data and a System for Sustainable Data Collection. New York, NY, USA: International Conference on Music Information Retrieval (ISMIR-16).
To download MedleyDB and MedleyDB 2.0 in their entirety, please request access via Zenodo at the following links. We will do our best to reply to requests promptly.
Please acknowledge our paper in academic research:
MedleyDB Original:
R. Bittner, J. Salamon, M. Tierney, M. Mauch, C. Cannam and J. P. Bello, "MedleyDB: A Multitrack Dataset for Annotation-Intensive MIR Research", in 15th International Society for Music Information Retrieval Conference, Taipei, Taiwan, Oct. 2014.
MedleyDB 2.0
Bittner, R., Wilkins, J., Yip, H., & Bello, J. (2016). MedleyDB 2.0: New Data and a System for Sustainable Data Collection. New York, NY, USA: International Conference on Music Information Retrieval (ISMIR-16).
Tools
We have developed a set of python scripts and tools to facilitate working with MedleyDB. The most current version of these tools can be found on the MedleyDB github repository.
MedleyDB is indexed in The Open Multitrack Testbed, which can be used to search for tracks with particular specifications.
MedleyDB is indexed in The Open Multitrack Testbed, which can be used to search for tracks with particular specifications.
