Video salient event classification using audio features

The aim of this work is to detect the events in video sequences that are salient with respect to the audio signal. In particular, we focus on the audio analysis of a video, with the goal of finding which are the significant features to detect audio-salient events. In our work we have extracted the audio tracks from videos of different sport events. For each video, we have manually labeled the salient audio-events using the binary markings. On each frame, features in both time and frequency domains have been considered. These features have been used to train different classifiers: Classification and Regression Trees, Support Vector Machine, and k-Nearest Neighbor. The classification performances are reported in terms of confusion matrices.