Data-driven decomposition of crowd noise from indoor sporting events

Abstract

Separating crowd responses from raw acoustic signals at sporting events is challenging because recordings contain complex combinations of acoustic sources, including crowd noise, music, individual voices, and public address (PA) systems. This paper presents a data-driven decomposition of recordings of 30 collegiate sporting events. The decomposition uses machine-learning methods to find three principal spectral shapes that separate various acoustic sources. First, the distributions of recorded one-half-second equivalent continuous sound levels from men's and women's basketball and volleyball games are analyzed with regard to crowd size and venue. Using 24 one-third-octave bands between 50 Hz and 10 kHz, spectrograms from each type of game are then analyzed. Based on principal component analysis, 87.5% of the spectral variation in the signals can be represented with three principal components, regardless of sport, venue, or crowd composition. Using the resulting three-dimensional component coefficient representation, a Gaussian mixture model clustering analysis finds nine different clusters. These clusters separate audibly distinct signals and represent various combinations of acoustic sources, including crowd noise, music, individual voices, and the PA system.