FMA: A Dataset For Music Analysis
Michaël Defferrard, Kirell Benzi, Pierre Vandergheynst, Xavier Bresson
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- github.com/mdeff/fmaOfficialIn papertf★ 0
- github.com/microsoft/fadtkpytorch★ 251
- github.com/darius522/dnr-utilsnone★ 73
- github.com/MorenoLaQuatra/ARCHpytorch★ 54
- github.com/cocktail-fork/cocktail-fork.github.ionone★ 5
- github.com/dcase2024-task7-sound-scene-synthesis/fadtkpytorch★ 4
- github.com/Manmayi/Music-Data-Visualizationnone★ 1
- github.com/karn1986/fma_pytorchpytorch★ 0
- github.com/KrishnaManmayi/Music-Data-Visualizationnone★ 0
- github.com/markcutajar/raw-music-tagging-cnnstf★ 0
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