000227512 001__ 227512
000227512 005__ 20190525071229.0
000227512 02470 $$a1612.01840$$2ArXiv
000227512 037__ $$aCONF
000227512 245__ $$aFMA: A Dataset For Music Analysis
000227512 269__ $$a2017
000227512 260__ $$c2017
000227512 336__ $$aConference Papers
000227512 520__ $$aWe introduce the Free Music Archive (FMA), an open and easily accessible dataset which can be used to evaluate several tasks in music information retrieval (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. By releasing the FMA, we hope to foster research which will improve the state-of-the-art and hopefully surpass the performance ceiling observed in e.g. genre recognition (MGR). The data is made of 106,574 tracks, 16,341 artists, 14,854 albums, arranged in a hierarchical taxonomy of 161 genres, for a total of 343 days of audio and 917 GiB, all under permissive Creative Commons licenses. It features metadata like song title, album, artist and genres; user data like play counts, favorites, and comments; free-form text like description, biography, and tags; together with full-length, high-quality audio, and some pre-computed features. We propose a train/validation/test split and three subsets: a genre-balanced set of 8,000 tracks from 8 major genres, a genre-unbalanced set of 25,000 tracks from 16 genres, and a 98 GiB version with clips trimmed to 30s. This paper describes the dataset and how it was created, proposes some tasks like music classification and annotation or recommendation, and evaluates some baselines for MGR. Code, data, and usage examples are available at https://github.com/mdeff/fma.
000227512 6531_ $$adataset
000227512 6531_ $$amusic information retrieval
000227512 6531_ $$aopen data
000227512 700__ $$0249515$$g226056$$aDefferrard, Michaël
000227512 700__ $$0245769$$g204172$$aBenzi, Kirell
000227512 700__ $$g120906$$aVandergheynst, Pierre$$0240428
000227512 700__ $$aBresson, Xavier$$g140163$$0241065
000227512 7112_ $$dOctober 23-28, 2017$$cSuzhou, China$$a18th International Society for Music Information Retrieval Conference
000227512 8564_ $$uhttps://github.com/mdeff/fma$$zURL
000227512 909C0 $$xU10380$$0252392$$pLTS2
000227512 909CO $$ooai:infoscience.tind.io:227512$$qGLOBAL_SET$$pconf$$pSTI
000227512 917Z8 $$x226056
000227512 917Z8 $$x226056
000227512 937__ $$aEPFL-CONF-227512
000227512 973__ $$rNON-REVIEWED$$sSUBMITTED$$aEPFL
000227512 980__ $$aCONF