Affective content analysis in comedy and horror videos by audio emotional event detection
We study the problem of affective content analysis. In this paper, we think of affective contents as those video/audio segments, which may cause an audience's strong reactions or special emotional experiences, such as laughing or fear. Those emotional factors are related to the users' attention, evaluation, and memories of the content. The modeling of affective effects depends on the video genres. In this work, we focus on comedy and horror films to extract the affective content by detecting a set of so-called audio emotional events (AEE) such as laughing, horror sounds, etc. Those AEE can be modeled by various audio processing techniques, and they can directly reflect an audience's emotion. We use the AEE as a clue to locate corresponding video segments. Domain knowledge is more or less employed at this stage. Our experimental dataset consists of 40-minutes comedy video and 40-minutes horror film. An average recall and precision of above 90% is achieved. It is shown that, in addition to rich visual information, an appropriate usage of special audios is an effective way to assist affective content analysis.