Audio-based automatic management of TV commercials

Although TV commercial identification and clustering are suitable applications for automatic multimedia indexing technology, they remain as problems still unsolved. Most current systems either require a big computational load and therefore can not be executed online, or just perform a detection, without clustering nor identification. In this paper two advertisement indexing approaches are presented: an off-line detection and clustering system and an online identification system, both based only on audio features for computational reasons. For the off-line clustering two metrics are evaluated, and an initial commercial boundary detection algorithm, based on identifying drop energy points which are also acoustic change boundaries, is presented. For the on-line system we analyze the response-time/identification scores constraints. Experiments performed on real data validate both off-line and on-line implementations as well as that audio only features are enough discriminant to detect and classify TV commercials.