A Multi-Class Audio Classification Method With Respect To Violent Content In Movies Using Bayesian Networks

Abstract

In this work, we present a multi-class classification algorithm for audio segments recorded from movies, focusing on the detection of violent content, for protecting sensitive social groups (e.g. children). Towards this end, we have used twelve audio features stemming from the nature of the signals under study. In order to classify the audio segments into six classes (three of them violent), Bayesian networks have been used in combination with the one versus all classification architecture. The overall system has been trained and tested on a large data set (5000 audio segments), recorded from more than 30 movies of several genres. Experiments showed, that the proposed method can be used as an accurate multi-class classification scheme, but also, as a binary classifier for the problem of violent -non violent audio content.