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SIC (Songs ans Instrumentals classification)

Replicable python code for the article:

@unpublished{Bayle2019,
  author = {Bayle, Yann and Hanna, Pierre and Robine, Matthias},
  title = {Content-based information retrieval for supervised classification of musical tags in real-world unbalanced datasets: Application to the Instrumentals and Songs},
  year = {2019},
  note = {Submitted to PLOS ONE}
}

Details

Task: content-based musical playlists generation focused on Songs and Instrumentals.

Musical Dataset: CCMixter, Jamendo, MedleyDB and SATIN.

Results: Our suggested approach generates an Instrumental playlist with up to three times less false positives than state-of-the-art.

Contributions:

  • The first review of SIC systems in the context of playlist generation.
  • The first formal design of experiment of the Song Instrumental Classification (SIC) task.
  • A demonstration that the use of frame features outperforms the use of global track features in the case of SIC and thus diminish the risk of an algorithm being a "Horse".
  • A knowledge-based SIC algorithm ---easily explainable--- that can process large musical database whereas state-of-the-art algorithms cannot.
  • A new track tagging method based on frame predictions that outperforms the Markov model in terms of accuracy and f-score.
  • A demonstration that better playlists related to a tag can be generated when the autotagging algorithm focuses only on this tag.

Requirements/Dependencies

Alternatives to consider for the SVD and SVS

Singing Voice Detection

Singing Voice Separation