Audio-based automatic management of TV commercials

Although TV commercial identification and clustering are suit-able 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 identi-fication. In this paper two advertisement indexing approaches are presented: an off-line detection and clustering system and an on-line 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 implemen-tations as well as that audio only features are enough discriminant to detect and classify TV commercials