Huge amounts of digital videos are being produced and broadcast
every day, leading to giant media archives. Effective techniques
are needed to make such data accessible further. Automatic
meta-data labelling of broadcast media is an essential task
for multimedia indexing, where it is standard to use multi-modal
input for such purposes. This paper describes a novel method
for automatic detection of media genre and show identities using
acoustic features, textual features or a combination thereof.
Furthermore the inclusion of available meta-data, such as time
of broadcast, is shown to lead to very high performance. Latent
Dirichlet Allocation is used to model both acoustics and text,
yielding fixed dimensional representations of media recordings
that can then be used in Support Vector Machines based classi-
fication. Experiments are conducted on more than 1200 hours
of TV broadcasts from the British Broadcasting Corporation
(BBC), where the task is to categorise the broadcasts into 8 genres
or 133 show identities. On a 200-hour test set, accuracies of
98.6% and 85.7% were achieved for genre and show identifi-
cation respectively, using a combination of acoustic and textual
features with meta-data.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)