  The world today is experiencing an abundance of music like no other time, and
attempts to group music into clusters have become increasingly prevalent.
Common standards for grouping music were songs, artists, and genres, with
artists or songs exploring similar genres of music seen as related. These
clustering attempts serve critical purposes for various stakeholders involved
in the music industry. For end users of music services, they may want to group
their music so that they can easily navigate inside their music library; for
music streaming platforms like Spotify, companies may want to establish a solid
dataset of related songs in order to successfully provide personalized music
recommendations and coherent playlists to their users. Due to increased
competition in the streaming market, platforms are trying their best to find
novel ways of learning similarities between audio to gain competitive
advantage. Our team, comprised of music lovers with different tastes, was
interested in the same issue, and created Music-Circles, an interactive
visualization of music from the Billboard. Music-Circles links audio feature
data offered by Spotify to popular songs to create unique vectors for each
song, and calculate similarities between these vectors to cluster them. Through
interacting with Music-Circles, users can gain understandings of audio
features, view characteristic trends in popular music, and find out which music
cluster they belong to.
