Jean-Bernard Rault

From audio recurrences to TV program structuring

This paper addresses the problem of unsupervised detection of recurrent audio segments in TV programs toward program structuring. Recurrent segments are the key elements in the process of program structuring. This allows a direct and non linear access to the main parts inside a program. It hence facilitates browsing within a recorded TV program or a program that is available on a TV-on-Demand service. Our work focuses on programs like entertainments, shows, magazines, news... It proposes an audio recurrence detection method. This is either applied over an episode or a set of episodes of the same program. Different types of audio descriptors are proposed and evaluated over an 65-hours video dataset corresponding to 112 episodes of TV programs.

A low-complexity audio fingerprinting technique for embedded applications

This paper proposes a new optimized audio-based fingerprinting technology for embedded applications. The target use case is related to TV content synchronization and its numerous applications for Social TV and second screen applications. The proposed technology can be used for automatically identifying the program being watched by capturing the sound of the TV set. It can also be used to know which program is being watched and to precisely estimate the timestamp of the currently broadcast moment with respect to the beginning of the program. This is very useful for second screen applications where notifications (e.g. quizzes, additional information, commercials) have to be sent to viewers with a perfect synchronization relatively to the broadcast TV program. The robustness of the proposed technique is first evaluated on a large music database and then by considering a realistic use case where this technology is embedded in a smartphone.