Overview
Like never before, the web has become a place for sharing
creative work - such as music - among a global community of artists and
art lovers. While music and music collections predate the web, the web
enabled much larger scale collections. Whereas people used to own a
handful of vinyls or CDs, they nowadays have instant access to the whole
of published musical content via online platforms. Such dramatic
increase in the size of music collections created two challenges: (i)
the need to automatically organize a collection (as users and publishers
cannot manage them manually anymore), and (ii) the need to
automatically recommend new songs to a user knowing his listening
habits. An underlying task in both those challenges is to be able to
group songs in semantic categories.
Music genres are categories that have arisen through a complex interplay of cultures, artists, and market forces to characterize similarities between compositions and organize music collections. Yet, the boundaries between genres still remain fuzzy, making the problem of music genre recognition (MGR) a nontrivial task (Scaringella 2006). While its utility has been debated, mostly because of its ambiguity and cultural definition, it is widely used and understood by end-users who find it useful to discuss musical categories (McKay 2006). As such, it is one of the most researched areas in the Music Information Retrieval (MIR) field (Sturm 2012).
The task of this challenge, one of the four official challenges of the Web Conference (WWW2018) challenges track, is to recognize the musical genre of a piece of music of which only a recording is available. Genres are broad, e.g. pop or rock, and each song only has one target genre. The data for this challenge comes from the recently published FMA dataset (Defferrard 2017), which is a dump of the Free Music Archive (FMA), an interactive library of high-quality and curated audio which is freely and openly available to the public.
Music genres are categories that have arisen through a complex interplay of cultures, artists, and market forces to characterize similarities between compositions and organize music collections. Yet, the boundaries between genres still remain fuzzy, making the problem of music genre recognition (MGR) a nontrivial task (Scaringella 2006). While its utility has been debated, mostly because of its ambiguity and cultural definition, it is widely used and understood by end-users who find it useful to discuss musical categories (McKay 2006). As such, it is one of the most researched areas in the Music Information Retrieval (MIR) field (Sturm 2012).
The task of this challenge, one of the four official challenges of the Web Conference (WWW2018) challenges track, is to recognize the musical genre of a piece of music of which only a recording is available. Genres are broad, e.g. pop or rock, and each song only has one target genre. The data for this challenge comes from the recently published FMA dataset (Defferrard 2017), which is a dump of the Free Music Archive (FMA), an interactive library of high-quality and curated audio which is freely and openly available to the public.
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