Abstract
Many media streams consist of distinct objects that repeat. For example, broadcast television and radio signals contain advertisements, call sign jingles, songs and even whole programs that repeat. The problem we address is to identify explicitly the underlying structure in repetitive streams and deconstruct them into their component objects. Our architecture assumes no a priori knowledge of the streams, and does not require a pre-trained database. Everything the system needs is learned on the fly. We demonstrate that using a modestly capable computer it is perfectly feasible to identify, in realtime, repeating objects that occur days or even weeks apart in audio or video streams. We outline the algorithms, enumerate several applications and present results from real streams.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
