BYU

Abstract by Spencer Wadsworth

Personal Infomation


Presenter's Name

Spencer Wadsworth

Co-Presenters

Dallen Stark

Degree Level

Undergraduate

Co-Authors

Dallen Stark

Abstract Infomation


Department

Physics and Astronomy

Faculty Advisor

Mark Transtrum

Title

Machine Learning Classifying of Crowd Acoustics from College Basketball Games

Abstract

While ML has been applied in numerous audio applications, the aim is usually to distinguish events from noise, rather than trying to characterize the noise itself. This presentation comprises an initial study using ML to characterize crowd dynamics during collegiate basketball games. High-fidelity crowd noise recordings from several men’s and women’s games were synchronized with game video and used to produce a training dataset for supervised ML by linking game events (e.g., baskets, fouls) with acoustic labels (e.g., cheering, silence, applause). Using the training dataset, a ML classifier, specifically a random forest classifier, was built and customized to identify causal game events from acoustic crowd responses. The initial findings are very promising having high levels of accuracy.  These findings are discussed, along with potential improvements from adjusting training data and  ML classifier selection.