Media Management Commission Seminar 2017 in Lugano. Day 1 - presentation 5

1. FIAT/IFTA MAM Survey 2017
Highlights from the results analysis
FIAT/IFTA MMC Seminar, Lugano 2017 – Brecht Declercq (VIAA), Gerhard Stanz (ORF) – 08.06.2017

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

5. disclaimer:
lies,
damn lies,
and statistics
[Benjamin Disraeli]

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

8. YOUR METADATA
CREATION STRATEGY

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

11. HARVESTING
FROM PRODUCTION

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%

13. Harvesting internal sources

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

16. JOURNALISTS/EDITORS
AS CATALOGERS?

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%
5%
10%
15%
20%
25%
30%
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*

32. DISCOVERABILITY
FINDABILITY

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!