Acoustic Detection of Violence in Real and Fictional Environments

Detecting violence is an important task due to the amount of people who suffer its effects daily. There is a tendency to focus the problem either in real situations or in non real ones, but both of them are useful on its own right. Until this day there has not been clear effort to try to relate both environments. In this work we try to detect violent situations on two different acoustic databases through the use of crossed information from one of them into the other. The system has been divided into three stages: feature extraction, feature selection based on genetic algorithms and classification to take a binary decision. Results focus on comparing performance loss when a database is evaluated with features selected on itself, or selection based in the other database. In general, complex classifiers tend to suffer higher losses, whereas simple classifiers, such as linear and quadratic detectors, offers less than a 10% loss in most situations.