Erotic Audio Recognition Using Heterogeneous Ensemble Classifiers

Abstract

In this paper, we present a novel approach to the task of automatic adult video detection based on feature porno-sounds recognition using multiple feature vectors and an ensemble of binary classifiers. In this framework, firstly the audio track is extracted and segmented into equal segments from an unknown video. Then each segment is split into sequences of voiced and unvoiced unequal fragments. Multiple feature vectors are extracted for each voiced fragments. Components classifiers including Support Vector Machine (SVM) are trained based on such feature vectors. At the classification, the outputs provided by the component classifiers are combined through fusion rules to form a final output of the ensemble. According to the classification results of these fragments, each segment is classified as or Finally, based on these segments results, the audio track or further the unknown video is labeled erotic or natural. Online and off-line experiments show that the proposed approach yields high performance than using single classifiers.