Just like a symphony orchestra where different instruments come together to create harmonious melodies, when people gather in a crowd, they also produce a complex symphony of sounds. A recent study aimed to understand the behavioral dynamics of crowds attending basketball games by analyzing the acoustic signals they generate. By using a method called recurrence quantification analysis (RQA), researchers were able to extract features from the crowd’s audio signals that reflect their behavioral interactions. RQA measures provided valuable insights into the recurrence patterns that underlie different crowd behaviors, such as angry noise, applause, cheer, distraction noise, positive chant, and negative chant. This research shows that by analyzing these acoustic dynamics, we can gain a deeper understanding of how crowds interact and express their emotions. Imagine if we could decode the unique language of crowd sound and predict their emotional state just like deciphering musical notes to understand a symphony. The findings open up exciting possibilities for improving crowd management, enhancing event experiences, and even predicting crowd reactions in various contexts. To learn more about this fascinating research, check out the full article!

Abstract
When multiple individuals interact in a conversation or as part of a large crowd, emergent structures and dynamics arise that are behavioral properties of the interacting group rather than of any individual member of that group. Recent work using traditional signal processing techniques and machine learning has demonstrated that global acoustic data recorded from a crowd at a basketball game can be used to classify emergent crowd behavior in terms of the crowd’s purported emotional state. We propose that the description of crowd behavior from such global acoustic data could benefit from nonlinear analysis methods derived from dynamical systems theory. Such methods have been used in recent research applying nonlinear methods to audio data extracted from music and group musical interactions. In this work, we used nonlinear analyses to extract features that are relevant to the behavioral interactions that underlie acoustic signals produced by a crowd attending a sporting event. We propose that recurrence dynamics measured from these audio signals via recurrence quantification analysis (RQA) reflect information about the behavioral dynamics of the crowd itself. We analyze these dynamics from acoustic signals recorded from crowds attending basketball games, and that were manually labeled according to the crowds’ emotional state across six categories: angry noise, applause, cheer, distraction noise, positive chant, and negative chant. We show that RQA measures are useful to differentiate the emergent acoustic behavioral dynamics between these categories, and can provide insight into the recurrence patterns that underlie crowd interactions.

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