Social Analytics for Game Day


  • Extracted audio features including A-weighted coefficients, Mel-frequency cepstrum from basketball games.
  • Performed crowd noise classification to identify instances of crowd cheers and boos for automatic highlight generation.
  • Utilizing random grid-search hyperparameter tuning for Random Forest classifier achieved 93% training accuracy.
  • Built a Support Vector Machine classifier to classify accelerometer data to gain insights about the audience’s behaviour and emotional response at a sporting event and attained 98% test accuracy.

 

Type: Other
Release Date: Apr 30, 2021
Last Updated: Apr 30, 2021

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