%0 Generic %1 defferrard2016dataset %A Defferrard, Michaël %A Benzi, Kirell %A Vandergheynst, Pierre %A Bresson, Xavier %D 2016 %K %T FMA: A Dataset For Music Analysis %U http://arxiv.org/abs/1612.01840 %X We introduce the Free Music Archive (FMA), an open and easily accessible dataset suitable for evaluating several tasks in MIR, a field concerned with browsing, searching, and organizing large music collections. The community's growing interest in feature and end-to-end learning is however restrained by the limited availability of large audio datasets. The FMA aims to overcome this hurdle by providing 917 GiB and 343 days of Creative Commons-licensed audio from 106,574 tracks from 16,341 artists and 14,854 albums, arranged in a hierarchical taxonomy of 161 genres. It provides full-length and high-quality audio, pre-computed features, together with track- and user-level metadata, tags, and free-form text such as biographies. We here describe the dataset and how it was created, propose a train/validation/test split and three subsets, discuss some suitable MIR tasks, and evaluate some baselines for genre recognition. Code, data, and usage examples are available at https://github.com/mdeff/fma

@misc{defferrard2016dataset, abstract = {We introduce the Free Music Archive (FMA), an open and easily accessible dataset suitable for evaluating several tasks in MIR, a field concerned with browsing, searching, and organizing large music collections. The community's growing interest in feature and end-to-end learning is however restrained by the limited availability of large audio datasets. The FMA aims to overcome this hurdle by providing 917 GiB and 343 days of Creative Commons-licensed audio from 106,574 tracks from 16,341 artists and 14,854 albums, arranged in a hierarchical taxonomy of 161 genres. It provides full-length and high-quality audio, pre-computed features, together with track- and user-level metadata, tags, and free-form text such as biographies. We here describe the dataset and how it was created, propose a train/validation/test split and three subsets, discuss some suitable MIR tasks, and evaluate some baselines for genre recognition. Code, data, and usage examples are available at https://github.com/mdeff/fma}, added-at = {2017-12-30T09:48:18.000+0100}, author = {Defferrard, Michaël and Benzi, Kirell and Vandergheynst, Pierre and Bresson, Xavier}, biburl = {https://www.bibsonomy.org/bibtex/209b2696374131c39d2d4da6d7d3a25b2/leerooy}, interhash = {cf19b13c199e1a06affe41628b5539e3}, intrahash = {09b2696374131c39d2d4da6d7d3a25b2}, keywords = {}, note = {cite arxiv:1612.01840Comment: ISMIR 2017 camera-ready}, timestamp = {2017-12-30T09:48:18.000+0100}, title = {FMA: A Dataset For Music Analysis}, url = {http://arxiv.org/abs/1612.01840}, year = 2016 }