About
A playlist is more than just a list of songs. It is a map of moods, habits, and intimate
rhythms. When platforms extract these signals as data, they become part of our
digital identity,
a computational profile built not from what we say, but from what we listen
to.
Playlist
visualiser
translates a Spotify playlist into a composite visual identity. The system extracts features
from the uploaded playlist URL and transforms them into a shifting field of orbs to visualise
listening habits. It reveals how personal taste becomes encoded as data, fluid in appearance yet
shaped by the simplifying logic of algorithmic classification.
Visual Archive
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Process Breakdown
⑴ Paste Playlist URL: Users begin by pasting their Spotify playlist URL
into
the input field. This URL allows the system to retrieve track-level audio features such as
energy, valence, tempo, acousticness, danceability, and more.
⑵ Load Playlist: Processes the playlist and produces a field of shifting
orbs.
Each orb corresponds to a statistical property of the playlist. Together they form a
composite
visual that represents how the system "reads" the user's listening habits.
⑶ Save Button: Captures the current visual state and downloads the image
locally. A thumbnail version is stored in the browser's internal archive so users can
revisit
later.
⑷ Archive Button: Opens a gallery of all previously generated visuals. This
allows users to compare how different playlists produce different computed visuals.
⑸ Reset View Button: Clears the canvas and returns the system to intial
state
and allow users to load a new playlist or re-explore their own.
⑹ System Classification Panel: System display of categories it infers from
the
playlist such as gender, age, and genre. This demonstrates how platforms reduce complex
listening habits into demographic labels, how taste can be simplified into segments through
the
mechanism of algorithmic profiling.