10.5281/zenodo.3889149
https://zenodo.org/records/3889149
oai:zenodo.org:3889149
Lee, Jongpil
Jongpil
Lee
0000-0002-1126-0081
KAIST
Bryan, Nicholas J.
Nicholas J.
Bryan
0000-0003-1469-7278
Adobe Research
Salamon, Justin
Justin
Salamon
0000-0001-6345-4593
Adobe Research
Jin, Zeyu
Zeyu
Jin
0000-0003-0161-5915
Adobe Research
Nam, Juhan
Juhan
Nam
0000-0003-2664-2119
KAIST
dim-sim
Zenodo
2020
music, similarity, search, triplets, user annotations, metric learning, dim-sim
2020-06-11
eng
10.1109/ICASSP40776.2020.9053442
10.5281/zenodo.3889148
https://zenodo.org/communities/ismir
https://zenodo.org/communities/ieee
https://zenodo.org/communities/mir
1.0.0
Creative Commons Attribution Non Commercial 4.0 International
The dim-sim dataset is a collection of user-annotated music similarity triplet ratings used to evaluate music similarity search and related algorithms. Our similarity ratings are linked to the Million Song Dataset (MSD) and were collected for the following paper:
Disentangled Multidimensional Metric Learning for Music Similarity
Jongpil Lee, Nicholas J. Bryan, Justin Salamon, Zeyu Jin, and Juhan Nam.
Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP). IEEE, 2020.
@inproceedings{Lee2019MusicSimilarity,
title={Disentangled Multidimensional Metric Learning For Music Similarity},
author={Lee, Jongpil and Bryan, Nicholas J. and Salamon, Justin and Jin, Zeyu, and Nam, Juhan},
booktitle={Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
year={2020},
organization={IEEE}
}
We kindly request that articles and other works in which this dataset is used cite the paper as listed above.
Please see our paper or visit https://jongpillee.github.io/multi-dim-music-sim for more information.