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ALF-200k: Towards Extensive Multimodal Analyses of Music Tracks and Playlists

  • Eva Zangerle
  • Michael Tschuggnall
  • Stefan Wurzinger
  • Günther Specht
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10772)

Abstract

In recent years, approaches in music information retrieval have been based on multimodal analyses of music incorporating audio as well as lyrics features. Because most of those approaches are lacking reusable, high-quality datasets, in this work we propose ALF-200k, a publicly available, novel dataset including 176 audio and lyrics features of more than 200,000 tracks and their attribution to more than 11,000 user-created playlists. While the dataset is of general purpose and thus, may be used in experiments for diverse music information retrieval problems, we present a first multimodal study on playlist features and particularly analyze, which type of features are shared within specific playlists and thus, characterize it. We show that while acoustic features act as the major glue between tracks contained in a playlists, also lyrics features are a powerful means to attribute tracks to playlists.

Keywords

Music information retrieval Multimodal dataset Lyrics features Audio features Playlist analyses Classification 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Eva Zangerle
    • 1
  • Michael Tschuggnall
    • 1
  • Stefan Wurzinger
    • 1
  • Günther Specht
    • 1
  1. 1.Department of Computer ScienceUniversität InnsbruckInnsbruckAustria

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