LAUSR

The relationship between crowd noise and crowd behavioral dynamics is a relatively unexplored field of research. Signal processing and machine learning (ML) may be useful in classifying and predicting crowd emotional state. As a precursor to performing ML, it is instructive to identify which crowd acoustic events an unsupervised ML algorithm would classify as unique. An initial set of audio features have been extracted from high-fidelity noise recordings of crowds at Brigham Young University men’s and women’s basketball games. A K-Means clustering analysis was conducted on half-second segments of the recordings using extracted features consisting of numerous statistical and spectral characteristics. For example, a clustering analysis performed on a one-twelfth-octave spectrogram of crowd noise recordings reveals there are approximately six unique events that occur during a game. The clusters for different audio feature sets are compared with human labeling of the different acoustical events. Implications for further ML algorithm development based on various sets of audio features are discussed.