# FMA: A Dataset For Music Analysis Source: [FMA: A Dataset For Music Analysis]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:34.152Z Authors: Micha\\"el Defferrard, Kirell Benzi, Pierre Vandergheynst, Xavier Bresson Published: 2016-12-06T14:58:59.000Z ## 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 ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Music Genre Classification on FMA small (test)](): Music Genre Classification; FMA small (test); Accuracy: 58. ## Original sources and implementation [Original paper]() [PDF]()