Mood Dynamic Playlist: Interpolating a musical path between emotions using a KNN algorithm

Abstract

We often to listen to music for its power to change our emotions. Whether selecting music for concentration, tunes for dancing, or lullabies for falling asleep, people often select music based on their desired mood or activity. We propose a method for automatically generating musical playlists that takes the listener on an emotional journey. We represent a playlist as a path of songs through the arousal-valence circumplex space, using existing datasets of songs annotated with affect values. Given a starting and desired affective state, we employ a K-nearest neighbor approach to choose songs that gradually and smoothly step through the affective space. We compare several different distance metrics and we evaluate the smoothness of the resulting playlists using mean squared error. We discuss an example playlist and link to a demonstration of our approach.

Keywords: Artificial Intelligence, Algorithm, Music

DOI: 10.54941/ahfe100894