Recognizing the genre (e.g. rock, pop, jazz, etc.) of a piece of music.
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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.
We here summarize our experience running a challenge with open data for musical genre recognition.
With this work, we propose to jointly learn the graph structure and the parameters of graph convolutional networks (GCNs) by approximately solving a bilevel program that learns a discrete probability distribution on the edges of the graph.
We found that features extracted from harmonic elements can satisfactorily predict music genre for the Brazilian case, as well as features obtained from the Spotify API.
SOTA for Music Genre Recognition on chords