Data Science for the Detection of Emerging Music Styles

  • De Bie, Tijl E P (Principal Investigator)

Project Details

StatusFinished
Effective start/end date1/11/141/11/16

Research Output

  • 3 Conference Contribution (Conference Proceeding)
  • 1 Conference Abstract
  • 1 Article (Academic Journal)

Subjectively Interesting Component Analysis: Data Projections that Contrast with Prior Expectations

Kang, B., Lijffijt, J., Santos-Rodriguez, R. & De Bie, T., 13 Aug 2016, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16. New York, NY, USA: Association for Computing Machinery (ACM), p. 1615-1624 10 p.

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Open Access
File
  • 4 Citations (Scopus)
    260 Downloads (Pure)

    SuMoTED: An intuitive edit distance between rooted unordered uniquely-labelled trees

    McVicar, M., Sach, B., Mesnage, C., Lijffijt, J., Spyropoulou, E. & De Bie, T., 1 Aug 2016, In : Pattern Recognition Letters. 79, p. 52-59 8 p.

    Research output: Contribution to journalArticle (Academic Journal)

    Open Access
    File
  • 5 Citations (Scopus)
    235 Downloads (Pure)

    Interactively exploring supply and demand in the UK independent music scene

    Mcvicar, M., Mesnage, C., Lijffijt, J. & De Bie, T., 2015, Machine Learning and Knowledge Discovery in Databases, Part III. Springer Berlin Heidelberg, Vol. 9286.

    Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)