Classification of Ontological Violence Content Detection through Audio Features and Supervised Learning

Abstract

Violence detection is one of the important aspects, which can be used in different applications. Based on the data format, the violence can be defined in many ways. This paper focused to develop an automatic violence detection framework from audio type data. To do this, a new and efficient set of features are extracted from the audio signals, which provides more discrimination between different types of violence types in audio signals. Considering both spatial and Mel frequency characteristics of audio signals, totally 12 statistical functionals are accomplished to define every signal. Furthermore, the violence is defined in an ontological fashion, such that the all possible violence types which signify the violent behavior are detected. Extensive simulations are carried out over the proposed detection framework by considering the audio signals extracted from different video clips ripped from different movies. The performance is analyzed through the Receiver Operating Characteristics like, Accuracy, Precision, Recall, and False Positive Rate and the obtained results verify the performance enhancement and show a better performance than the conventional approaches.

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