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Huge amounts of digital videos are being produced and broadcast every day,leading to giant media archives. Effective techniques are needed to make suchdata accessible further. Automatic meta-data labelling of broadcast media is anessential task for multimedia indexing, where it is standard to use multi-modalinput for such purposes. This paper describes a novel method for automaticdetection of media genre and show identities using acoustic features, textualfeatures or a combination thereof. Furthermore the inclusion of availablemeta-data, such as time of broadcast, is shown to lead to very highperformance. Latent Dirichlet Allocation is used to model both acoustics andtext, yielding fixed dimensional representations of media recordings that canthen be used in Support Vector Machines based classification. Experiments areconducted on more than 1200 hours of TV broadcasts from the BritishBroadcasting Corporation (BBC), where the task is to categorise the broadcastsinto 8 genres or 133 show identities. On a 200-hour test set, accuracies of98.6% and 85.7% were achieved for genre and show identification respectively,using a combination of acoustic and textual features with meta-data.

Automatic Genre and Show Identification of Broadcast Media