Abstract

Scene boundary detection is crucial in applications like scene segmentation and video skimming, but the presence of ad clips makes it difficult. There are some existing methods based on visual, audiovisual and audio-only features. The existing audio-only feature-based method depends on short silences between program and ad or between ads. Still, short silences can also be present inside the program, affecting performance. So we proposed a detection method for ad shots using MFCC features. MFCC is a basic speech feature based on short-term spectral. We used the average of MFCC for a shot and determined the threshold for detection purposes. We used four episodes of The Big Bang Theory as our dataset. Our method is the first method that uses MFCC features for ad detection and explores the relation between video contents and MFCC features.