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Mood-based Classification ofTV Programmes            Jana Eggink, Sam Davies, Denise Bland                          BBC R&...
Searching the Archives•   BBC aims to open up its archives for public access by 2022•   Limited metadata available     • T...
Which Moods?       Evaluation               Potency                     Activity      Happy – Sad         Serious – Humoro...
User Trial• 200 members of the general public• 544 video clips (3 minutes excerpts)• Each labelled by at least 6 participa...
Inter-rater Agreement                                   DoKrippendorff’s Alpha           1                                ...
Correlation  •           Which moods are independent?  •           Do observed correlations correspond to the EPA model?  ...
ClassificationVideo clips• 444 in development set, 3-fold cross validation• 100 in holdout setFeatures• Audio (MFCCs, ampl...
Automatic Classification Gives Good ResultsClear moods only• 2 class problem                  Classification Accuracy• >95...
Automatic Classification Gives Good ResultsAverage rates                                  RMS Error for detailed moods• 1-...
Conclusions•   There is general agreement about mood for TV programme clips•   Mood perception is dominated by two dimensi...
Demo   R&D   BBC MMXIII
Usage of the Redux Mood GUI•   Usage data 14th May 2012 to 22nd August 2012•   3206 unique users, nearly a third (1013) ar...
Search Behaviour   R&D             BBC MMXIII
Programmes Watched            Frequent Programmes                     Watched            Never Mind the Buzzcocks         ...
Outliers attract Attention    R&D                      BBC MMXIII
Outlook and Future Work•   Public facing Mood GUI based on iPlayer•   Available; http://moods.ch.bbc.co.uk•   Requires gre...
Outlook and future work•   Integration of pre-existing metadata     R&D                                   BBC MMXIII
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Mood-based Classification of TV Programmes - Jana Eggink, Sam Davies, Denise Bland (Semantic Media @ BBC, Feb 2013)

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This talk was given by Jana Eggink, Sam Davies and Denise Bland (BBC R&D) at the "Semantic Media @ BBC" event on 6 February 2013.

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Mood-based Classification of TV Programmes - Jana Eggink, Sam Davies, Denise Bland (Semantic Media @ BBC, Feb 2013)

  1. 1. Mood-based Classification ofTV Programmes Jana Eggink, Sam Davies, Denise Bland BBC R&D {jana.eggink, sam.davies, denise.bland}@bbc.co.ukR&D BBC MMXIII
  2. 2. Searching the Archives• BBC aims to open up its archives for public access by 2022• Limited metadata available • Title • Broadcast date • Genre (mostly) • Limited: actors, semantic labels for professional use• Mood as additional metadata, intuitive understanding R&D BBC MMXIII
  3. 3. Which Moods? Evaluation Potency Activity Happy – Sad Serious – Humorous Fast paced – Slow paced Light-hearted – Dark Exciting – RelaxingInteresting – Boring (EPA model based on Osgood et al, 1957) R&D BBC MMXIII
  4. 4. User Trial• 200 members of the general public• 544 video clips (3 minutes excerpts)• Each labelled by at least 6 participants R&D BBC MMXIII
  5. 5. Inter-rater Agreement DoKrippendorff’s Alpha 1 De Agreement about Mood Labels perfect random R&D BBC MMXIII
  6. 6. Correlation • Which moods are independent? • Do observed correlations correspond to the EPA model? Correlation PCA 1 light 0.9 0.6 fast excitinginterest 0.8 fast 0.4 0.7 dark 0.6 0.2 Component 2 interest sad serious 24% variance 0.5 0 0.4 humorous exciting happy 0.3 -0.2 light-heartedhumorous 0.2 -0.4 0.1 relaxing happy 0 -0.6 slow boring happy humorous exciting interest fast light -0.6 -0.4 -0.2 0 0.2 0.4 0.6 Component 1 63% variance R&D BBC MMXIII
  7. 7. ClassificationVideo clips• 444 in development set, 3-fold cross validation• 100 in holdout setFeatures• Audio (MFCCs, amplitude, zero-crossing, spectral centroid and roll-off)• Video (face, luminance, cuts, motion)• Genre (human assigned)Testing• Clips with very clear moods only• Average rates, all clips on a 1 to 5 scale R&D BBC MMXIII
  8. 8. Automatic Classification Gives Good ResultsClear moods only• 2 class problem Classification Accuracy• >95% correct for serious/humorous• ~90% correct for slow/fast-paced R&D BBC MMXIII
  9. 9. Automatic Classification Gives Good ResultsAverage rates RMS Error for detailed moods• 1-5 scale• ~0.7 RMSE for serious/humorous• <0.7 RMSE for slow/fast-paced R&D BBC MMXIII
  10. 10. Conclusions• There is general agreement about mood for TV programme clips• Mood perception is dominated by two dimensions• Classification for clips with clear moods is very accurate, and still possible on a detailed continuous scale• Both genre labels and signal processing features are useful • Humorous-serious is strongly related to genre • Slow/fast-paced can be better modelled by audio/video features Eggink & Bland, A Large Scale Experiment for Mood-Based Classification of TV Programmes, IEEE Int. Conf. Multimedia and Expo, ICME2012, also as BBC White Paper Nr. 232 R&D BBC MMXIII
  11. 11. Demo R&D BBC MMXIII
  12. 12. Usage of the Redux Mood GUI• Usage data 14th May 2012 to 22nd August 2012• 3206 unique users, nearly a third (1013) are returning users R&D BBC MMXIII
  13. 13. Search Behaviour R&D BBC MMXIII
  14. 14. Programmes Watched Frequent Programmes Watched Never Mind the Buzzcocks 258 Torchwood 90 Dr Finlay`s Casebook 74 An Evening in with David Attenborough 55 Holiday Weatherview 49 Would I Lie to You? 46 Never Mind the Buzzcocks 36 Morecambe and Wise 33 Never Mind the Buzzcocks 32 Till Death Us Do Part 32 R&D BBC MMXIII
  15. 15. Outliers attract Attention R&D BBC MMXIII
  16. 16. Outlook and Future Work• Public facing Mood GUI based on iPlayer• Available; http://moods.ch.bbc.co.uk• Requires greater research in UX R&D BBC MMXIII
  17. 17. Outlook and future work• Integration of pre-existing metadata R&D BBC MMXIII

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