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Ambient Spotify

Automated Playlist Controller using iPhone sensor data for activity detection

While popular music streaming apps have developed strong models to curate playlists based on an individual’s music taste, they are currently unable to adapt to a user’s specific context – their live current setting or activity. Ambient Spotify is a system that leverages the sensors usually found on a smartphone – such as gyroscope and accelerometer on the embedded intertial measuring unit (IMU), combined with publicly available time and weather data – to create inference models and gather context clues to automatically select playlists matching the user’s surrounding context and activity. 

Date: May 2024

Time: Two Weeks

Collaborators: Elizabeth Li, Matthew Jeung

Main Tools Used: Swift, CoreML, Spotify API

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