Doulaty, M., Saz, O., Ng, R.W.M. et al. (1 more author) (2016) Automatic Genre and Show Identification of Broadcast Media. In: Proceedings of the 17th Annual Conference of the International Speech Communication Association (Interspeech). Interspeech 2016, 08-12 Sep 2016, San Francisco. ISCA
Abstract
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 ISCA. This is an author produced version of a paper subsequently published in Proceedings of the 17th Annual Conference of the International Speech Communication Association (Interspeech). Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | genre identification; show identification; broadcast media automatic labelling; latent Dirichlet allocation |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC) UNSPECIFIED |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 14 Dec 2016 14:55 |
Last Modified: | 19 Dec 2022 13:35 |
Published Version: | http://doi.org/10.21437/Interspeech.2016 |
Status: | Published |
Publisher: | ISCA |
Refereed: | Yes |
Identification Number: | 10.21437/Interspeech.2016 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:109228 |