Detecting news reporting using audio/visual information

This paper proposes an integrated approach to discriminate news reporting from everything else in broadcast news data based on both audio and visual information. The separation of news reporting segments from others not only can provide useful indices for video streams but also serves as a pre-processing step for tasks such as speaker identification and speech recognition so that only speech segments are passed on for further processing. A set of audio and visual features are adopted that aim at capturing the intrinsic properties of the underlying classes. Four types of classifiers (threshold fuzzy, Gaussian Mixture Model based, and Support Vector Machine) are tested. Some of the experimental results are presented and discussed in the paper.