Аннотация
We 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.
Описание
FMA: A Dataset For Music Analysis
Линки и ресурсы
тэги