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2nd MAM Survey (DECLERCQ)

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FIAT/IFTA World Conference 2017 Mexico City

Publicado en: Datos y análisis
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2nd MAM Survey (DECLERCQ)

  1. 1. FIAT/IFTA MAM Survey 2017 Highlights from the results analysis FIAT/IFTA World Conference, Mexico City 2017 – Brecht Declercq (VIAA) – 21.10.2017
  2. 2. Why the MMC MAM Surveys?  Archive world is changing fast  You find cutting edge archive knowledge almost only in the archives  FIAT/IFTA members are in search of answers  MMC collects, processes and distributes know-how  2nd Survey since the one in 2015
  3. 3. disclaimer: lies, damn lies, and statistics [Benjamin Disraeli]
  4. 4. Broadcaster’s archives 40 71% Regional / national AVarchives 14 25% Others 2 4% MAM SURVEY 2017 RESPONDENTS Number of responses 56 Number of unique archives 53
  5. 5. YOUR METADATA CREATION STRATEGY
  6. 6. Metadata creation methods classification • manually: manual annotation in the archive • harvesting: importing existing data from production • mining: algorithms generate new meaning from input data
  7. 7. 96% 78% 67% 54% 48% 31% 24% 15% 9% 60% 39% 50% 23% 39% 36% 21% 25% 32% Manual, internal Import from production: other systems Import from production: planning system Copy and paste from online sources Import from production: closed captioning Manual, external commercial service Automatic feature extraction: speech-to-text Manual, external non-commercial Automatic feature extraction: other technologies how many respondents use this method at all? for which share of your items on average? (non-users excl.) Metadata creation methods
  8. 8. HARVESTING FROM PRODUCTION
  9. 9. Harvesting potential average The amount of metadata that can be harvested in your case? 36% How much could it be if the integration would be perfect? 58%
  10. 10. Harvesting internal sources
  11. 11. Harvesting external sources 63% 31% 31% 19% 6% 0% 20% 40% 60% 80% 100% Others IMDB Geonames DBPedia ISAN Other: Musicbrainz Baidu Soccerway Ogol Wikipedia Sharedog UFC site Tribune (TMS)
  12. 12. Harvesting ‘Edit Decisions’ (EDL) 69% 31% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% no yes
  13. 13. JOURNALISTS/EDITORS AS CATALOGERS?
  14. 14. Production staff fill in metadata themselves? 57% 44% 11% 2% 43% 26% 15% 2% total broadcaster regional / national archive other yes no
  15. 15. 0 1 2 3 4 5 6 7 8 9 10 Happy with the quality? ENTIRELY HAPPY  NOT HAPPY AT ALL 
  16. 16. 0 1 2 3 4 5 6 7 8 9 10 Happy with the delay? ENTIRELY HAPPY  NOT HAPPY AT ALL 
  17. 17. MININGAKA AUTOMATIC FEATURE EXTRACTION
  18. 18. Mining experience and expectation 1.video-OCR 2.logodetection 3.image/concept- recognition 4.object recognition 5.semanticsound classification 6.technicalsound classification 7.speaker identification 8.music recognition 9.aspectratio qualification 10.subject headings 11.speech-to-text A. in daily work 2% 2% 2% 0% 4% 9% 6% 4% 20% 9% 10% B. Tested, we will implement it 6% 4% 6% 4% 2% 2% 6% 9% 6% 7% 12% C. no experience, but useful 67% 46% 69% 63% 56% 50% 61% 63% 46% 69% 46% D. no experience, not relevant 9% 43% 15% 19% 28% 35% 15% 19% 26% 7% 10% E. tested, but not good enough 17% 6% 9% 15% 11% 4% 13% 6% 2% 7% 22% MAX MIN
  19. 19. 17% 17% 67% spoken word audio / radio only video / TV / moving image only both Speech-to-text: for which kind of media?
  20. 20. 0% 0% 0% 0% 0% 0% 17% 33% 50% 100% 0% 50% 100% Other Publicity Feature films Cartoons / animation Children's programs Fiction / drama Shows / quiz Current affairs / factual Sports News Speech-to-text: for which genres?
  21. 21. 0 1 2 3 4 5 6 7 8 9 10 Speech-to-text: happy with the quality? ENTIRELY HAPPY  NOT HAPPY AT ALL 
  22. 22. 0 1 2 3 4 5 6 7 8 9 10 Speech-to-text: happy with names recognition? ENTIRELY HAPPY  NOT HAPPY AT ALL 
  23. 23. Why don’t you use speech-to-text (yet)? 23% 13% 13% 11% 9% 13% 9% 4% 5% 11% Not good enough for our main languages How to integrate it in MAM architecture? Costs will outweigh benefits Our MAM doesn't allow it Implementation is busy Others: in some stage of preparation Others: no MAM yet / too early Others: we use subtitling, so low priority Others (We have it already)
  24. 24. METADATA CREATION IN THE FUTURE
  25. 25. Remaining manual annotation 4% 11% 7% 23% 14% 9% 7% 5% 0% 2% 0% 14% 0% 20% 40% 60% 80% 100% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% nodecrease Numberofrespondents Decrease of manual annotation until…
  26. 26. COMBINED SEARCH
  27. 27. Combined search possible? 47.3% 34.5% 18.2% 0% 20% 40% 60% 80% 100% yes no n/a
  28. 28. Combined search details Database Sources: Anonymous1 Anonymous2 Anonymous3 Anonymous4 Anonymous5 Anonymous6 Anonymous7 Anonymous8 Anonymous9 Anonymous10 Anonymous11 Anonymous12 Anonymous13 Anonymous14 4with3Sources 7with2Sources Television / Video Archive 14 x x x x x x x x x x x x x x Radio / Audio Archive 13 x x x x x x x x x x x x x Photo, image, graphics database 9 x x x x x x x x x Document Management System 5 x x x x x Newspapers 7 x x x x x x x Television License Database 5 x x x x x Television Planning System 5 x x x x x Radio License Database 5 x x x x x Radio Planning System 4 x x x x MAM 1 x Newswires, Agency Information 2 x x other 2 x x Administrative Data 1 x Books and Manuscripts 1 x Web-Archive 1 x Number of Databases: 9 7 7 7 5 5 5 5 5 4 4 4 4 4 3 2 happy with the search possibilities as they are now*
  29. 29. DISCOVERABILITY FINDABILITY
  30. 30. 91% 9% 0% 20% 40% 60% 80% 100% no yes Use of external registries ISAN, ISBN, GUID, …
  31. 31. Archival clips on Youtube? 17% 22% 61% 0% 20% 40% 60% 80% 100% yes, incl. internal metadata yes, excl. internal metadata no
  32. 32. Metadata for target groups other than production? 45% 36% 50% 43% 16% 36% 50% 22% 34% 14% 28% 5% 14% 7% broadcaster's archives national/regional Avarchives others total no we don't yes, from the beginning yes, from later on other
  33. 33. Adaptations on metadata for other target groups? 35% 19% 19% 11% 43% we adapt the vocabulary used we adapt the metadata categories we decrease the level of detail other no we don't adapt our metadata
  34. 34. THANK YOU! FIAT/IFTA MMC & PMC Members Eléonore Alquier (INA) Elena Brodie-Kusa (EBK) Anne Couteux (INA) Brecht Declercq (VIAA) Gerhard Stanz (ORF) More and deeper results in de publication after this conference!

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