Identification of informative acoustic features in the transition from non-violent to violent crowd behavior

Human crowds can exhibit a variety of collective behaviors. Here, we explore the transition from peaceful to violent behavior in human crowds using acoustic data. Predicting when a crowd will transition from a peaceful to a violent state has many potential applications, such as peace-keeping and security. Relative to video, acoustic data is easier to obtain and is less affected by lighting conditions, such during night or in dark areas. We apply machine learning methods to a data set that includes both video and audio recordings of violent and non-violent crowds. Previous results showed that audio data was only marginally less effective than video alone for classifying violent/non-violent scenes. In this work, I conduct a feature-importance study to identify which acoustic metrics are most informative for correctly classifying peaceful and violent crowds.