Unsupervised feature extraction for multimedia event detection and ranking using audio content

In this paper, we propose a new approach to classify and rank multimedia events based purely on audio content using video data from TRECVID-2013 multimedia event detection (MED) challenge. We perform several layers of nonlinear mappings to extract a set of unsupervised features from an initial set of temporal and spectral features to obtain a superior presentation of the atomic audio units. Additionally, we propose a novel weighted divergence measure for kernel based classifiers. The extensive set of experiments confirms that augmentation of the proposed steps results in an improved accuracy for most of the event classes.