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DTSTART:20190310T070000
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UID:69b5554c75847
DTSTART;TZID=America/Toronto:20190816T103000
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URL:https://uwaterloo.ca/artificial-intelligence-group/events/masters-thesi
 s-presentation-modelling-chart-trajectories
LOCATION:DC - William G. Davis Computer Research Centre 200 University Aven
 ue West 2310 Waterloo ON N2L 3G1 Canada
SUMMARY:Master’s Thesis Presentation: Modelling Chart Trajectories Using\
 nSong FeaturesExport this event to calendar
CLASS:PUBLIC
DESCRIPTION:JONATHAN VI PERRIE\, MASTER’S CANDIDATE\n_David R. Cheriton S
 chool of Computer Science_\n\nOver the years\, hit song science has been a
  controversial topic within\nmusic information retrieval (MIR). Researcher
 s have debated whether an\nunbiased dataset can be constructed and what it
  means to successfully\nmodel song performance. Often classes for modellin
 g are derived from\none component of song performance\, like for example\,
  a song's peak\nposition on some chart. 
DTSTAMP:20260314T123212Z
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