BYU

Abstract by Eric Todd

Personal Infomation


Presenter's Name

Eric Todd

Co-Presenters

None

Degree Level

Undergraduate

Co-Authors

None

Abstract Infomation


Department

Physics and Astronomy

Faculty Advisor

Mark Transtrum

Title

Modeling Crowd Noise with Machine Learning

Abstract

Understanding crowd behavior is an important, yet complex problem that stretches across multiple fields of study. In this project, we focus on the problem of modeling crowd tendencies from different acoustical features of crowd noise. We use both audio and video recordings of crowd sentiment to train machine learning models. I examine some of the challenges we have encountered in approaching such a complex problem, such as isolating and extracting key acoustical events, as well as selecting machine learning techniques. I discuss my role in data collection, and in developing methods to extract key acoustical features relevant to our model, in addition to researching applicable machine learning models. Our long-term objective is to better understand acoustical signals that could predict changes in a crowd’s emotion which could in turn indicate changes and shifts in crowd behavior.