Spotify Playlist Organization - Mood-Based Cluster Analysis

This paper aimed to explore ways to organize Spotify playlists, relying on clustering algorithms. Clustering algorithms were performed on playlists with extracted and standardized audio features obtained from the Spotify API, and the algorithms used were KMeans, DBSCAN, Affinity Propagation, and Spe...

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Bibliographic Details
Main Authors: Krilasevic, Admir, Masetic, Zerina, Keco, Dino
Format: Conference Proceeding
Language:English
Subjects:
Clustering
Clustering algorithms
DBSCAN
Feature extraction
Focusing
Inspection
Kmeans
Manuals
Organizations
Playlist
Spotify
Standards organizations
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Summary:This paper aimed to explore ways to organize Spotify playlists, relying on clustering algorithms. Clustering algorithms were performed on playlists with extracted and standardized audio features obtained from the Spotify API, and the algorithms used were KMeans, DBSCAN, Affinity Propagation, and Spectral Clustering. Their performances were measured with the silhouette score, execution time, and inspection of clustered tracks, where it was determined that KMeans was the best algorithm in this case. Even though the execution time of KMeans is the third best, its silhouette score is the highest with 0.263. With this model, it is possible to effectively perform a mood-based organization of one's Spotify playlist, by dividing it into multiple smaller ones that share similar audio features.
ISSN:2767-9470
DOI:10.1109/INFOTEH60418.2024.10495953