dim-sim

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.