2nd MAM Survey (DECLERCQ)
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FIAT/IFTA World Conference 2017 Mexico City
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2nd MAM Survey (DECLERCQ)
- 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. 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
- 7. 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
- 9. Metadata creation methods classification • manually: manual annotation in the archive • harvesting: importing existing data from production • mining: algorithms generate new meaning from input data
- 10. 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
- 12. 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%
- 14. 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)
- 15. Harvesting ‘Edit Decisions’ (EDL) 69% 31% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% no yes
- 17. Production staff fill in metadata themselves? 57% 44% 11% 2% 43% 26% 15% 2% total broadcaster regional / national archive other yes no
- 18. 0 1 2 3 4 5 6 7 8 9 10 Happy with the quality? ENTIRELY HAPPY NOT HAPPY AT ALL
- 19. 0 1 2 3 4 5 6 7 8 9 10 Happy with the delay? ENTIRELY HAPPY NOT HAPPY AT ALL
- 20. MININGAKA AUTOMATIC FEATURE EXTRACTION
- 21. 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
- 22. 17% 17% 67% spoken word audio / radio only video / TV / moving image only both Speech-to-text: for which kind of media?
- 23. 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?
- 24. 0 1 2 3 4 5 6 7 8 9 10 Speech-to-text: happy with the quality? ENTIRELY HAPPY NOT HAPPY AT ALL
- 25. 0 1 2 3 4 5 6 7 8 9 10 Speech-to-text: happy with names recognition? ENTIRELY HAPPY NOT HAPPY AT ALL
- 26. 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)
- 27. METADATA CREATION IN THE FUTURE
- 28. 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…
- 29. COMBINED SEARCH
- 30. Combined search possible? 47.3% 34.5% 18.2% 0% 20% 40% 60% 80% 100% yes no n/a
- 31. 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*
- 33. 91% 9% 0% 20% 40% 60% 80% 100% no yes Use of external registries ISAN, ISBN, GUID, …
- 34. Archival clips on Youtube? 17% 22% 61% 0% 20% 40% 60% 80% 100% yes, incl. internal metadata yes, excl. internal metadata no
- 35. 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
- 36. 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
- 37. 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!
Notes de l'éditeur
- A little disclaimer... By Benjamin Disraeli I will today be presenting you: LIES, DAMN LIES... and STATISTICS
- Categorizing metadata creation methods can be done in many ways. You can rank them according to: when it happens during the media lifecycle: before, during, after the production, during re-use etc. who creates the metadata: the producer, the archivist, the media consumer etc. with which kind of tools: production tools, automatic analysis tools, the eye, the brain and the hands, … Etc. For this survey we have chosen to work mainly with these three categories: manual, harvesting and mining.
- QUESTION 1. Manual, internal metadata annotation by archivists / catalogers is – no surprise – the most popular method, with 57% of items on average cataloged via this method. Import from all kinds of production methods come next. Interesting to know is also that no less than 1 out of 3 archives calls for the help of an external, commercial service. And if such services are used, they are used for quite a bit of items: those who use it, use it for 36% of their items on average. Speech-to-text is already in use at 1 out of 4 archives. The ones who use it, do so for a bit more than 1 out of 5 of their items. And one last one: 1 out of 10 respondents indicated they use already other automatic feature extraction tools. But if they do so, they are quite popular, because they’re used for 1 out of 3 of their items on a daily basis on average. STT: 1/5 is relatief!!
- QUESTION B2. Harvesting vs. Mining Currently round 40% of the relevant metadata is imported into archive systems Even though some institutions regard 100% achievable, the average estimate still is 60% of the necessary metadata to be importable Some words on reasons (technical / organisational) - Image Description – still manual for quite some time List of Metadata-Elements and Challenges - later in the Publication
- NEW B3
- QUESTION D12. 16 Answers Category “Sonstige” = other ist the largest, which means .. … there seem to be by far more than were known to us, as we were creating the question Probably an interesting task for the MMC to make a list of such external sources in order to exchange their strength and weaknesses an general tipps how to use them. Volunteers step forward.
- QUESTION D10. Already one third, more than I would have expected
- QUESTION 6.
- QUESTION 9. How happy you are with the quality of the metadata put in by the production staff is very scattered, to say the least. On average you are judging them with a score of 5,48 out of 10.
- QUESTION 9. Also the delay of the metadata put in by the production staff is very scattered. On average you are judging them with a score of 5,70 out of 10.
- QUESTION E14. Algorithms that we know of – 5 questions indicating an always higher/lower level of approval. If 22 has really been understood is questionable – probably confused with simple 4:3 vs 16:9 and does not imply anamourphous 4:3 or detecting true 4:3 LBOX ratio (10 % = 9 Archives) Explanation of the “Mining Algorithms” This time Speech to Text as the MAX Archivists are Optimists (open towards the possibilities of technology) Proof that 15. and 23 are indeed concisiosly choosen Speech to text as most critically seen !! Antagonisms this Area. Probably the expectatiosn where to high.
- QUESTION 26. 6 out of 54 respondents use speech-to-text in the archive. It is clear that they consider it mostly a tool for as well audio as for video.
- QUESTION 27.
- QUESTION 28. The overall judgment about the quality of the speech-to-text result is not so bad after all, although one archive had a bad experience. The average result is a fair 6,83.
- QUESTION 29. As the recognition of proper nouns and entities is often more problematic, we’ve asked specifically about that. Also here the results are not so negative except in one case. Bu with an average of 6,66 there still seems to be a gap between what archivists would like and what the industry can deliver.
- QUESTION 31.
- Distribution between manual an automated (Harvesting AND Mining) annotation.
- QUESTION H34. Again this 30 to 40% remaining manual
- QUESTION G42. 50% have no distributed Search Possibility What does the relatively high figure of almost 20% not applicables mean (small “one database” institutions?)
- QUESTION G43. Of 25 Institutions that can search in multiple databases at once, one third has more than 4 Databases searchable 12 are satisfied with their multiple search capabilities, the others want some more databases
- Distribution between manual an automated (Harvesting AND Mining) annotation.
- QUESTION 36.
- QUESTION 38.
- QUESTION 40. An interesting conclusion here, is that broadcasters take into account the needs of other user groups than audiovisual production EXACTLY AS MUCH AS NATIONAL / REGIONAL ARCHIVES DO. The only difference is that the broadcasters do it only at a later stage, while the national / regional archives tend more to do it from the beginning. In general 1 out of 2 archives DOES take into account the metadata needs of other target groups.
- QUESTION 41.
- QUESTION 48.