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TU	
  Graz	
  Recording	
  Services:	
  Overview	
  

History	
  and	
  development	
  
Introducing	
  recording	
  services	
  
       General	
  manual	
  recording	
  
       Manual	
  streaming	
  
       Automated	
  recording	
  and	
  streaming	
  (prototype)	
  
Facts	
  and	
  didacCcs	
  
Project	
  –	
  Recordings	
  for	
  LifeLongLearning	
  
       Searchable	
  recordings	
  by	
  indexing	
  screencasts	
  
       Automated	
  audio	
  post-­‐processing	
  
       Automated	
  recordings	
  
History	
  and	
  Development	
  I	
  

Past	
  
• 2006	
  –	
  Start	
  of	
  PodcasCng-­‐Service	
  
Simple	
  screening	
  and	
  audio	
  recording	
  with	
  Camtasia	
  –	
  50%	
  failed	
  J	
  
First	
  efforts	
  for	
  automated	
  postprocessing	
  

• 2007	
  –	
  LifeCme	
  PodcasCng	
  
1st	
  Austrian	
  Podcast	
  Conference	
  in	
  cooperaCon	
  with	
  iUNIg	
  

• 2008	
  –	
  Start	
  with	
  (live-­‐)Streaming-­‐Service	
  
Live	
  Screening,	
  audio	
  and	
  video	
  recording	
  on	
  ePresence	
  Server	
  (Desire2Learn)	
  
hZp://curry.tugraz.at	
  	
  

• 2009	
  –	
  Start	
  of	
  iTunes	
  U	
  pla^orm	
  for	
  TU	
  Graz	
  
hZp://itunes.tugraz.at/series	
  	
  

• 2010	
  –	
  Start	
  of	
  Project:	
  Recordings	
  for	
  LifeLongLearning	
  
• 2010	
  –	
  Start	
  of	
  Project:	
  Automated	
  Recording	
  	
  
• 2011	
  –	
  Start	
  of	
  Subproject:	
  Searchable	
  Recordings	
  
Sta?onary	
  workflow	
  version	
  
History	
  and	
  Development	
  II	
  


Ongoing	
  Developments	
  and	
  Future	
  
• Since	
  2010	
  –	
  Project:	
  Recordings	
  for	
  LifeLongLearning	
  	
  
Overall	
  project	
  in	
  the	
  field	
  of	
  recordings	
  
• Since	
  2010	
  –	
  Automated	
  Lecture	
  Recordings	
  
Focus:	
  Workflow	
  and	
  usability	
  improvement	
  for	
  recordings	
  
Fully	
  automated	
  recording	
  and	
  postprocessing	
  of	
  lectures	
  

• Since	
  2011	
  –	
  Searchable	
  Recordings	
  
Focus:	
  Independent	
  workflow	
  version	
  
DocumentaCon	
  

• Since	
  2011	
  –	
  Automated	
  Audio-­‐Postprocessing:	
  
CooperaCon	
  with	
  Georg	
  Holzmann	
  from	
  „auphonic“	
  
Focus:	
  Speech	
  RecogniCon	
  
Recording	
  Services	
  -­‐	
  Overall	
  
Recording	
  Services	
  –	
  General	
  Recording	
  
Recording	
  Services	
  –	
  Streaming	
  I	
  
Recording	
  Services	
  –	
  Streaming	
  II	
  
                hEp://curry.tugraz.at	
  
Recording	
  Services	
  -­‐	
  Devices	
  
Strategy:	
  Open	
  EducaConal	
  Resources	
  




                                                              hEp://opencontent.tugraz.at	
  
                         Model	
  by	
  Schaffert	
  (Schaffert,	
  2010)	
  adapted	
  to	
  TU	
  Graz	
  IniCaCves	
  
Facts	
  of	
  PodcasCng	
  Service	
  I	
  


600

             Number of Recordings
500
            Recording Time (h)

400


300


200


100


  0
      WS06 SS07 WS07 SS08 WS08 SS09 WS09 SS10 WS10 SS11 WS11 SS12
Facts	
  of	
  PodcasCng	
  Service	
  II	
  


4000

3500         Total Number of Recordings

3000         Total Recording Time (h)


2500

2000

1500

1000

500

   0
       WS06 SS07 WS07 SS08 WS08 SS09 WS09 SS10 WS10 SS11 WS11 SS12
DidacCcs	
  and	
  Workflow	
  I	
  


DidacCcs	
  and	
  Purposes	
  
• General	
  Recording	
  (Screening	
  /	
  Audio	
  /	
  Video)	
  
Full	
  Recording	
  of	
  lesson	
  
Pre-­‐	
  or	
  Postrecording	
  at	
  office	
  
Tutorial	
  and	
  instrucConal	
  sequences	
  
Process	
  centered	
  content	
  
Short	
  clips	
  for	
  help-­‐center	
  

• Live	
  Streaming	
  (Screening	
  /	
  Audio	
  /	
  Video)	
  
Blended	
  learning	
  	
  
Mass	
  courses	
  
Special	
  events	
  

• iTunes	
  U	
  
„Selected“	
  media-­‐files	
  for	
  Public	
  RelaCons	
  
DidacCcs	
  and	
  Workflow	
  II	
  

Workflow	
  of	
  General	
  Recording	
  
•  Framework	
  
   Agreement	
  with	
  teacher,	
  recording	
  details,	
  copyright	
  aspects	
  

•  Preprocess	
  
   Check	
  of	
  hardware,	
  sojware,	
  lecture	
  room	
  condiCons	
  
   Wireless	
  microphone,	
  Tablet	
  PC	
  
   Camtasia,	
  iShow	
  U	
  

•  Recording	
  
   Minimal	
  or	
  full	
  assistance	
  

•  Postprocess	
  
   Audio	
  opCmizaCon	
  
   Text	
  to	
  Search	
  processing	
  (indexing	
  screencasts)	
  
   Pruduc?on	
  of	
  end-­‐formats	
  (Flash	
  with	
  Search,	
  MP4)	
  	
  
   HTML	
  5	
  environment	
  (to	
  be	
  programmed)	
  

•  Publishing	
  
   on	
  TU	
  Graz	
  TeachCenter	
  (LMS)	
  
Project	
  –	
  Recordings	
  for	
  LifeLongLearning	
  I	
  

•  Project	
  framework	
  
    Period:	
  2010/01	
  to	
  2012/12	
  
    In	
  the	
  course	
  of	
  „Leistungsvereinbarungen“	
  
    Budget:	
  ap.	
  100.000€	
  

•  Project	
  partner	
  
    TU	
  Graz:	
  Office	
  for	
  LifeLongLearning:	
  hZp://lifelonglearning.tugraz.at	
  	
  
    TU	
  Graz:	
  Dept.	
  Social	
  Learning:	
  hZp://elearning.tugraz.at	
  	
  
    TU	
  Graz:	
  Dept.	
  InformaCon	
  Design	
  &	
  Media	
  
    Associated	
  partner:	
  Auphonic:	
  hZps://auphonic.com	
  	
  

•  Project	
  focus	
  
    General	
  topic:	
  invesCgaCons	
  on	
  recordings	
  for	
  lifelonglearning	
  at	
  universiCes	
  
    Subjects:	
  DidacCc	
  scenarios	
  for	
  recordings	
  
             	
  EvaluaCon	
  of	
  recording	
  aciCvites	
  
             	
  PotenCal	
  of	
  recording	
  services	
  for	
  general	
  university	
  pracCce	
  
Project	
  –	
  Recordings	
  for	
  LifeLongLearning	
  II	
  
•  Project	
  investments	
  
    Personal:	
  ap.	
  40h/w;	
  4	
  people	
  (20	
  h/w,	
  10	
  h/w,	
  on	
  demand)	
  
    Equipment:	
  several	
  hardware	
  for	
  recording	
  purposes	
  
           	
  set	
  up	
  hardware	
  for	
  automated	
  recording	
  	
  

•  Project	
  efforts	
  
    EvaluaCons:	
  Hardcopy	
  polls	
  of	
  4	
  very	
  different	
  lectures	
  
            	
  Automated	
  evaluaCons	
  of	
  streaming	
  server	
  data	
  
    Developments:	
  indexing	
  screencasts	
  for	
  text-­‐searching	
  videos	
  
            	
  fully	
  automated	
  recording	
  systems	
  for	
  lecture	
  rooms	
  
    University	
  pracCce:	
  LLL-­‐Course	
  „Reniraumtechnik“	
  (planned)	
  

•  PublicaCons	
  	
  
    	
  
    Grigoriadis,	
  Y.;	
  S?ckel,	
  C.;	
  Schön,	
  M.;	
  Nagler,	
  W.;	
  Ebner,	
  M.;	
  Automated	
  Podcas?ng	
  System	
  for	
  Universi?es.	
  -­‐	
  
    in:	
  Conference	
  Proceedings	
  ICL	
  2012.	
  (in	
  print).	
  	
  
    	
  
    Ebner,	
  M.;	
  Nagler,	
  W.;	
  Schön,	
  M.:	
  Have	
  They	
  Changed?	
  Five	
  Years	
  of	
  Survey	
  on	
  Academic	
  Net-­‐GeneraCon.	
  -­‐	
  in:	
  
    Proceedings	
  of	
  World	
  Conference	
  on	
  EducaConal	
  MulCmedia,	
  Hypermedia	
  and	
  TelecommunicaCons	
  (2012),	
  S.	
  
    343	
  –	
  353,	
  World	
  Conference	
  on	
  EducaConal	
  MulCmedia,	
  Hypermedia	
  and	
  TelecommunicaCons	
  ;	
  2012	
  
    	
  
    Grigoriadis,	
  Y.;	
  Fickert,	
  L.;	
  Ebner,	
  M.;	
  Schön,	
  M.;	
  Nagler,	
  W.:	
  Podcas?ng	
  for	
  Electrical	
  Power	
  Systems.	
  -­‐	
  in:	
  
    Conference	
  Proceedings	
  MIPRO	
  2012.	
  (2012),	
  S.	
  1412	
  -­‐	
  1417	
  
    	
  
    Schön,	
  M.;	
  Ebner,	
  M.;	
  Kothmeier,	
  G.:	
  It's	
  Just	
  About	
  Learning	
  the	
  Mul?plica?on	
  Table.	
  -­‐	
  in:	
  LAK12	
  -­‐	
  2nd	
  
    InternaConal	
  Conference	
  on	
  Learning	
  AnalyCcs	
  &	
  Knowledge.	
  (2012),	
  S.	
  1	
  –	
  8	
  
    	
  
    Nagler,	
  W.;	
  Grigoriadis,	
  Y.;	
  S?ckel,	
  C.;	
  Ebner,	
  M.:	
  Capture	
  Your	
  University.	
  -­‐	
  in:	
  IADIS	
  InternaConal	
  Conference	
  
    e-­‐Learning	
  ;	
  2010	
  (2010),	
  S.	
  139	
  -­‐	
  144	
  
Searchable	
  Recordings	
  by	
  Indexing	
  Screencasts	
  I	
  

•  Part	
  of	
  the	
  Project	
  –	
  Recordings	
  for	
  LifeLongLearning	
  	
  
•  Aim:	
  Make	
  recordings	
  searchable	
  
           	
  Full	
  length	
  lecture	
  recording	
  –	
  45,	
  90	
  min	
  or	
  more	
  
                  	
  typically	
  contains	
  slides	
  of	
  a	
  presentaCon	
  



•  Methode:	
  Generate	
  index	
  from	
  extracted	
  text	
  
     	
  
     Key	
  technology:	
  OCR:	
  opCcal	
  character	
  recogniCon	
  
     	
  
     Input:	
  screencast	
  
     Output:	
  encoded	
  video	
  embedded	
  in	
  flash	
  player	
  with	
  a	
  ToC	
  (Table	
  of	
  Content)	
  
     and	
  a	
  word	
  search	
  field	
  
     	
  
     Problem:	
  OCR	
  sojware	
  is	
  not	
  compaCble	
  with	
  video	
  files	
  
     SoluCon:	
  frame	
  extracCon	
  
Searchable	
  Recordings	
  by	
  Indexing	
  Screencasts	
  II	
  
        •  What	
  sojware	
  to	
  use?	
  
        •  Which	
  frame	
  to	
  extract?	
  
        •  Are	
  all	
  extracted	
  frames	
  useful?	
  


The	
  frames	
  can	
  be	
  thought	
  of	
  as	
  a	
  sequence:	
       ConsecuCve	
  frames	
  tend	
  to	
  be	
  
           	
  ...,	
  f	
  [n–1],	
  f	
  [n],	
  f	
  [n+1],	
  ...	
     very	
  similiar	
  in	
  content	
  
	
  IF	
                                                                    This	
  allows	
  for	
  discarding	
  of	
  
           	
  |fs[n–1]	
  –	
  fs[n]|	
  <	
  S	
  	
                      repeCCve	
  data	
  
OR	
                                                                        Lost	
  data	
  can	
  be	
  later	
  constructed	
  
           	
  j[n]	
  –	
  j[n–1]	
  <	
  T	
                              from	
  neighbour	
  frames	
  
THEN	
                                                                      DetecCon	
  of	
  frames	
  with	
  
           	
  discard	
  the	
  current	
  frame	
  f	
  [n]	
             significant	
  content	
  changes	
  	
  
	
  
with	
  
n:	
  number	
  of	
  the	
  frame	
  
fs:	
  size	
  in	
  bytes	
  
j:	
  Cme	
  in	
  ms	
  
S:	
  deviaCon	
  parameter	
  for	
  the	
  size	
  
T:	
  deviaCon	
  parameter	
  for	
  the	
  Cme	
  
Searchable	
  Recordings	
  by	
  Indexing	
  Screencasts	
  IV	
  

Frame	
  extracCon	
  Sojware:	
  	
                            Encoding	
  a	
  video	
  file:	
  
                                                                	
  
              	
  FFmpeg	
  	
  hZp://ffmpeg.org	
  	
           $	
  ffmpeg	
  -­‐i	
  <inputfile>	
  -­‐ac	
  1	
  -­‐ab	
  40k	
  -­‐vcodec	
  
                                                                libx264	
  -­‐fpre	
  <codec_preset>	
  -­‐crf	
  23	
  -­‐vstats_file	
  
                                                                <outputfile>	
  
	
                                                              	
  
                                                                -­‐i:	
  name	
  of	
  the	
  input	
  video	
  file	
  
                                                                -­‐ac:	
  number	
  of	
  audio	
  channels	
  
Frame	
  selecCon:	
  FFmpeg	
  (-­‐vstats	
  opCon)	
          -­‐ab:	
  audio	
  bitrate	
  
                                                                -­‐vcodec:	
  video	
  codec	
  library	
  
              	
  local	
  „I“	
  frames	
                      -­‐crf:	
  constant	
  rate	
  factor	
  
                                                                -­‐vstats_file:	
  generaCon	
  of	
  -­‐vstats	
  file	
  
              	
  extract	
  Cmestamps	
                        	
  
                                                                	
  
	
                                                              ExtracCng	
  a	
  specific	
  frame	
  from	
  a	
  video	
  file:	
  
                                                                	
  
Further	
  frame	
  sorCng:	
  Perl	
  hZp://perl.org	
  	
     $	
  ffmpeg	
  -­‐ss	
  <offset>	
  -­‐i	
  <inputfile>	
  -­‐an	
  -­‐vframes	
  
                                                                1	
  -­‐qscale	
  1	
  <outputfile>	
  
                                                                	
  
              	
  size	
                                        -­‐ss	
  offset:	
  (Cme	
  of	
  frame	
  to	
  be	
  extracted)	
  in	
  seconds	
  
                                                                -­‐an:	
  no	
  audio	
  
              	
  posiCon	
  	
                                 -­‐vframes:	
  number	
  of	
  consequent	
  frames	
  to	
  extract	
  
                                                                -­‐qscale:	
  quality	
  factor	
  (1[best]	
  to	
  31[worst])	
  
Searchable	
  Recordings	
  by	
  Indexing	
  Screencasts	
  V	
  
Searchable	
  Recordings	
  by	
  Indexing	
  Screencasts	
  VI	
  

•  OCR	
  procedure:	
  
   	
  
   Extracted	
  frames	
  are	
  sent	
  to	
  OCR	
  sojware	
  
   	
  
   OCR	
  returns	
  one	
  text	
  file	
  for	
  each	
  frame	
  
   	
  
   Name	
  of	
  tex^ile	
  contains	
  Cming	
  info	
  
   	
  
   InformaCon	
  from	
  the	
  text	
  files	
  is	
  collected	
  
   and	
  used	
  for	
  ToC	
  


•  OCR	
  sojware	
  runs	
  on	
  iMac	
  using	
  
   Windows	
  7	
  through	
  virtualbox	
  
•  OCR	
  has	
  „hot	
  folder“	
  quality:	
  starts	
  
   operaCng	
  at	
  folder	
  input	
  automaCcally	
  
Searchable	
  Recordings	
  by	
  Indexing	
  Screencasts	
  VII	
  




Method	
  
implemented	
  in	
  
summer	
  2011	
  
SCll	
  under	
  further	
  
development	
  
Automated	
  Audio	
  Postprocessing	
  

•  CooperaCon	
  with	
  „auphonic“	
  
•  auphonic	
  supports	
  a	
  well	
  funcConing	
  service	
  according	
  to	
  
   audio	
  processing	
  for	
  free:	
  
   	
  
   „We	
  develop	
  new	
  algorithms	
  in	
  the	
  area	
  of	
  music	
  informa7on	
  
   retrieval	
  and	
  audio	
  signal	
  processing	
  to	
  create	
  an	
  automa7c	
  
   audio	
  post	
  produc7on	
  web	
  service	
  for	
  podcasts,	
  audio	
  books,	
  
   lecture	
  recordings,	
  screencasts,	
  etc.”	
  	
  
	
  
•  auphonic	
  offers	
  an	
  API	
  for	
  automated	
  upl-­‐	
  and	
  download	
  of	
  
   audio	
  files	
  to	
  be	
  processed	
  
•  hZps://auphonic.com/api-­‐docs/index.html	
  	
  
       	
  
Automated	
  Recording	
  I	
  
Automated	
  Recording	
  II	
  
Automated	
  Recording	
  III	
  

•  Crestron	
  media	
  control	
  panel	
  at	
  lecture	
  hall	
  	
  
•  Epiphan	
  Lecture	
  recorder	
  X2	
  controlled	
  via	
  RS-­‐232	
  API	
  by	
  Creston	
  
       Audio	
  signal:	
  single	
  channel	
  mix-­‐up	
  from	
  the	
  audio	
  mixer	
  of	
  lecture	
  hall	
  

       Video	
  SD	
  channel	
  by	
  SANYO	
  IPCam	
  
       Video	
  HD	
  channel	
  by	
  laptop	
  video	
  signal;	
  resoluCon	
  projector:	
  1280x960	
  	
  
       automated	
  scaling	
  up	
  to	
  HD	
  1920x1080	
  (under	
  construcCon)	
  

       HD,	
  SD	
  and	
  audio	
  are	
  saved	
  separated	
  in	
  a	
  mulC-­‐track	
  AVI	
  file.	
  

•  Transfer	
  from	
  X2	
  to	
  Streaming	
  Server	
  using	
  Intranet	
  FTP	
  	
  
•  Streaming	
  Server	
  Hardware:	
  Lynx	
  CALLEO	
  ApplicaCon	
  Server	
  4250	
  
       16	
  Core	
  CPU`s;	
  64	
  GB	
  RAM;	
  20	
  TB	
  HDD	
  Space	
  

•  Streaming	
  Server	
  Sojware:	
  wowza	
  3.0.3	
  on	
  Windows	
  2008	
  server	
  
   (controlled	
  using	
  RDP	
  protocol)	
  
       For	
  manual	
  streaming	
  with	
  epresence	
  and	
  	
  automated	
  recording	
  with	
  epiphan	
  X2	
  

       MulCcasCng	
  
Automated	
  Recording	
  IV	
  
Automated	
  Recording	
  V	
  

•  Finalising	
  of	
  automated	
  post-­‐processing	
  
•  Focus	
  on	
  speech	
  recogniCon	
  
•  Introducing	
  a	
  calendar	
  based	
  booking	
  system	
  connected	
  or	
  
   implemented	
  in	
  the	
  university	
  administraCon	
  pla^orm	
  
   (TUGRAZonline)	
  	
  
       All	
  lecture	
  hall	
  control	
  panels	
  are	
  connected	
  to	
  TUGRAZonline	
  

•  Discussion	
  about:	
  
       automated	
  start	
  and	
  stopp	
  of	
  recordings	
  due	
  to	
  booking	
  system	
  

       legality	
  aspects:	
  works	
  councils,	
  copyright	
  …	
  

•  Prototype	
  at	
  HS	
  13	
  working	
  since	
  2012	
  
•  7	
  more	
  systems	
  are	
  planned	
  to	
  start	
  in	
  autumn	
  2012	
  
•  Streaming	
  to	
  lecture	
  halls	
  
Contact	
  
TU	
  Graz	
  –	
  Dept.	
  Social	
  Learning:	
  Team	
  Podcas?ng	
  
Walther	
  Nagler	
  
YpaCos	
  Grigoriadis	
  	
  
Wolfgang	
  Hauer	
  
ChrisCan	
  SCckel	
  
	
  
walther.nagler@tugraz.at	
  
ypaCos@gmail.com	
  	
  
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  Social	
  Learning	
  (TU	
  Graz)	
  
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  sociallearning	
  
hZp://elearning.tugraz.at	
  	
  

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Automated Podcasting System for Universities

  • 1. TU  Graz  Recording  Services:  Overview   History  and  development   Introducing  recording  services   General  manual  recording   Manual  streaming   Automated  recording  and  streaming  (prototype)   Facts  and  didacCcs   Project  –  Recordings  for  LifeLongLearning   Searchable  recordings  by  indexing  screencasts   Automated  audio  post-­‐processing   Automated  recordings  
  • 2. History  and  Development  I   Past   • 2006  –  Start  of  PodcasCng-­‐Service   Simple  screening  and  audio  recording  with  Camtasia  –  50%  failed  J   First  efforts  for  automated  postprocessing   • 2007  –  LifeCme  PodcasCng   1st  Austrian  Podcast  Conference  in  cooperaCon  with  iUNIg   • 2008  –  Start  with  (live-­‐)Streaming-­‐Service   Live  Screening,  audio  and  video  recording  on  ePresence  Server  (Desire2Learn)   hZp://curry.tugraz.at     • 2009  –  Start  of  iTunes  U  pla^orm  for  TU  Graz   hZp://itunes.tugraz.at/series     • 2010  –  Start  of  Project:  Recordings  for  LifeLongLearning   • 2010  –  Start  of  Project:  Automated  Recording     • 2011  –  Start  of  Subproject:  Searchable  Recordings   Sta?onary  workflow  version  
  • 3. History  and  Development  II   Ongoing  Developments  and  Future   • Since  2010  –  Project:  Recordings  for  LifeLongLearning     Overall  project  in  the  field  of  recordings   • Since  2010  –  Automated  Lecture  Recordings   Focus:  Workflow  and  usability  improvement  for  recordings   Fully  automated  recording  and  postprocessing  of  lectures   • Since  2011  –  Searchable  Recordings   Focus:  Independent  workflow  version   DocumentaCon   • Since  2011  –  Automated  Audio-­‐Postprocessing:   CooperaCon  with  Georg  Holzmann  from  „auphonic“   Focus:  Speech  RecogniCon  
  • 5. Recording  Services  –  General  Recording  
  • 6. Recording  Services  –  Streaming  I  
  • 7. Recording  Services  –  Streaming  II   hEp://curry.tugraz.at  
  • 9. Strategy:  Open  EducaConal  Resources   hEp://opencontent.tugraz.at   Model  by  Schaffert  (Schaffert,  2010)  adapted  to  TU  Graz  IniCaCves  
  • 10. Facts  of  PodcasCng  Service  I   600 Number of Recordings 500 Recording Time (h) 400 300 200 100 0 WS06 SS07 WS07 SS08 WS08 SS09 WS09 SS10 WS10 SS11 WS11 SS12
  • 11. Facts  of  PodcasCng  Service  II   4000 3500 Total Number of Recordings 3000 Total Recording Time (h) 2500 2000 1500 1000 500 0 WS06 SS07 WS07 SS08 WS08 SS09 WS09 SS10 WS10 SS11 WS11 SS12
  • 12. DidacCcs  and  Workflow  I   DidacCcs  and  Purposes   • General  Recording  (Screening  /  Audio  /  Video)   Full  Recording  of  lesson   Pre-­‐  or  Postrecording  at  office   Tutorial  and  instrucConal  sequences   Process  centered  content   Short  clips  for  help-­‐center   • Live  Streaming  (Screening  /  Audio  /  Video)   Blended  learning     Mass  courses   Special  events   • iTunes  U   „Selected“  media-­‐files  for  Public  RelaCons  
  • 13. DidacCcs  and  Workflow  II   Workflow  of  General  Recording   •  Framework   Agreement  with  teacher,  recording  details,  copyright  aspects   •  Preprocess   Check  of  hardware,  sojware,  lecture  room  condiCons   Wireless  microphone,  Tablet  PC   Camtasia,  iShow  U   •  Recording   Minimal  or  full  assistance   •  Postprocess   Audio  opCmizaCon   Text  to  Search  processing  (indexing  screencasts)   Pruduc?on  of  end-­‐formats  (Flash  with  Search,  MP4)     HTML  5  environment  (to  be  programmed)   •  Publishing   on  TU  Graz  TeachCenter  (LMS)  
  • 14. Project  –  Recordings  for  LifeLongLearning  I   •  Project  framework   Period:  2010/01  to  2012/12   In  the  course  of  „Leistungsvereinbarungen“   Budget:  ap.  100.000€   •  Project  partner   TU  Graz:  Office  for  LifeLongLearning:  hZp://lifelonglearning.tugraz.at     TU  Graz:  Dept.  Social  Learning:  hZp://elearning.tugraz.at     TU  Graz:  Dept.  InformaCon  Design  &  Media   Associated  partner:  Auphonic:  hZps://auphonic.com     •  Project  focus   General  topic:  invesCgaCons  on  recordings  for  lifelonglearning  at  universiCes   Subjects:  DidacCc  scenarios  for  recordings    EvaluaCon  of  recording  aciCvites    PotenCal  of  recording  services  for  general  university  pracCce  
  • 15. Project  –  Recordings  for  LifeLongLearning  II   •  Project  investments   Personal:  ap.  40h/w;  4  people  (20  h/w,  10  h/w,  on  demand)   Equipment:  several  hardware  for  recording  purposes    set  up  hardware  for  automated  recording     •  Project  efforts   EvaluaCons:  Hardcopy  polls  of  4  very  different  lectures    Automated  evaluaCons  of  streaming  server  data   Developments:  indexing  screencasts  for  text-­‐searching  videos    fully  automated  recording  systems  for  lecture  rooms   University  pracCce:  LLL-­‐Course  „Reniraumtechnik“  (planned)   •  PublicaCons       Grigoriadis,  Y.;  S?ckel,  C.;  Schön,  M.;  Nagler,  W.;  Ebner,  M.;  Automated  Podcas?ng  System  for  Universi?es.  -­‐   in:  Conference  Proceedings  ICL  2012.  (in  print).       Ebner,  M.;  Nagler,  W.;  Schön,  M.:  Have  They  Changed?  Five  Years  of  Survey  on  Academic  Net-­‐GeneraCon.  -­‐  in:   Proceedings  of  World  Conference  on  EducaConal  MulCmedia,  Hypermedia  and  TelecommunicaCons  (2012),  S.   343  –  353,  World  Conference  on  EducaConal  MulCmedia,  Hypermedia  and  TelecommunicaCons  ;  2012     Grigoriadis,  Y.;  Fickert,  L.;  Ebner,  M.;  Schön,  M.;  Nagler,  W.:  Podcas?ng  for  Electrical  Power  Systems.  -­‐  in:   Conference  Proceedings  MIPRO  2012.  (2012),  S.  1412  -­‐  1417     Schön,  M.;  Ebner,  M.;  Kothmeier,  G.:  It's  Just  About  Learning  the  Mul?plica?on  Table.  -­‐  in:  LAK12  -­‐  2nd   InternaConal  Conference  on  Learning  AnalyCcs  &  Knowledge.  (2012),  S.  1  –  8     Nagler,  W.;  Grigoriadis,  Y.;  S?ckel,  C.;  Ebner,  M.:  Capture  Your  University.  -­‐  in:  IADIS  InternaConal  Conference   e-­‐Learning  ;  2010  (2010),  S.  139  -­‐  144  
  • 16. Searchable  Recordings  by  Indexing  Screencasts  I   •  Part  of  the  Project  –  Recordings  for  LifeLongLearning     •  Aim:  Make  recordings  searchable    Full  length  lecture  recording  –  45,  90  min  or  more    typically  contains  slides  of  a  presentaCon   •  Methode:  Generate  index  from  extracted  text     Key  technology:  OCR:  opCcal  character  recogniCon     Input:  screencast   Output:  encoded  video  embedded  in  flash  player  with  a  ToC  (Table  of  Content)   and  a  word  search  field     Problem:  OCR  sojware  is  not  compaCble  with  video  files   SoluCon:  frame  extracCon  
  • 17. Searchable  Recordings  by  Indexing  Screencasts  II   •  What  sojware  to  use?   •  Which  frame  to  extract?   •  Are  all  extracted  frames  useful?   The  frames  can  be  thought  of  as  a  sequence:   ConsecuCve  frames  tend  to  be    ...,  f  [n–1],  f  [n],  f  [n+1],  ...   very  similiar  in  content    IF   This  allows  for  discarding  of    |fs[n–1]  –  fs[n]|  <  S     repeCCve  data   OR   Lost  data  can  be  later  constructed    j[n]  –  j[n–1]  <  T   from  neighbour  frames   THEN   DetecCon  of  frames  with    discard  the  current  frame  f  [n]   significant  content  changes       with   n:  number  of  the  frame   fs:  size  in  bytes   j:  Cme  in  ms   S:  deviaCon  parameter  for  the  size   T:  deviaCon  parameter  for  the  Cme  
  • 18. Searchable  Recordings  by  Indexing  Screencasts  IV   Frame  extracCon  Sojware:     Encoding  a  video  file:      FFmpeg    hZp://ffmpeg.org     $  ffmpeg  -­‐i  <inputfile>  -­‐ac  1  -­‐ab  40k  -­‐vcodec   libx264  -­‐fpre  <codec_preset>  -­‐crf  23  -­‐vstats_file   <outputfile>       -­‐i:  name  of  the  input  video  file   -­‐ac:  number  of  audio  channels   Frame  selecCon:  FFmpeg  (-­‐vstats  opCon)   -­‐ab:  audio  bitrate   -­‐vcodec:  video  codec  library    local  „I“  frames   -­‐crf:  constant  rate  factor   -­‐vstats_file:  generaCon  of  -­‐vstats  file    extract  Cmestamps         ExtracCng  a  specific  frame  from  a  video  file:     Further  frame  sorCng:  Perl  hZp://perl.org     $  ffmpeg  -­‐ss  <offset>  -­‐i  <inputfile>  -­‐an  -­‐vframes   1  -­‐qscale  1  <outputfile>      size   -­‐ss  offset:  (Cme  of  frame  to  be  extracted)  in  seconds   -­‐an:  no  audio    posiCon     -­‐vframes:  number  of  consequent  frames  to  extract   -­‐qscale:  quality  factor  (1[best]  to  31[worst])  
  • 19. Searchable  Recordings  by  Indexing  Screencasts  V  
  • 20. Searchable  Recordings  by  Indexing  Screencasts  VI   •  OCR  procedure:     Extracted  frames  are  sent  to  OCR  sojware     OCR  returns  one  text  file  for  each  frame     Name  of  tex^ile  contains  Cming  info     InformaCon  from  the  text  files  is  collected   and  used  for  ToC   •  OCR  sojware  runs  on  iMac  using   Windows  7  through  virtualbox   •  OCR  has  „hot  folder“  quality:  starts   operaCng  at  folder  input  automaCcally  
  • 21. Searchable  Recordings  by  Indexing  Screencasts  VII   Method   implemented  in   summer  2011   SCll  under  further   development  
  • 22. Automated  Audio  Postprocessing   •  CooperaCon  with  „auphonic“   •  auphonic  supports  a  well  funcConing  service  according  to   audio  processing  for  free:     „We  develop  new  algorithms  in  the  area  of  music  informa7on   retrieval  and  audio  signal  processing  to  create  an  automa7c   audio  post  produc7on  web  service  for  podcasts,  audio  books,   lecture  recordings,  screencasts,  etc.”       •  auphonic  offers  an  API  for  automated  upl-­‐  and  download  of   audio  files  to  be  processed   •  hZps://auphonic.com/api-­‐docs/index.html      
  • 25. Automated  Recording  III   •  Crestron  media  control  panel  at  lecture  hall     •  Epiphan  Lecture  recorder  X2  controlled  via  RS-­‐232  API  by  Creston   Audio  signal:  single  channel  mix-­‐up  from  the  audio  mixer  of  lecture  hall   Video  SD  channel  by  SANYO  IPCam   Video  HD  channel  by  laptop  video  signal;  resoluCon  projector:  1280x960     automated  scaling  up  to  HD  1920x1080  (under  construcCon)   HD,  SD  and  audio  are  saved  separated  in  a  mulC-­‐track  AVI  file.   •  Transfer  from  X2  to  Streaming  Server  using  Intranet  FTP     •  Streaming  Server  Hardware:  Lynx  CALLEO  ApplicaCon  Server  4250   16  Core  CPU`s;  64  GB  RAM;  20  TB  HDD  Space   •  Streaming  Server  Sojware:  wowza  3.0.3  on  Windows  2008  server   (controlled  using  RDP  protocol)   For  manual  streaming  with  epresence  and    automated  recording  with  epiphan  X2   MulCcasCng  
  • 27. Automated  Recording  V   •  Finalising  of  automated  post-­‐processing   •  Focus  on  speech  recogniCon   •  Introducing  a  calendar  based  booking  system  connected  or   implemented  in  the  university  administraCon  pla^orm   (TUGRAZonline)     All  lecture  hall  control  panels  are  connected  to  TUGRAZonline   •  Discussion  about:   automated  start  and  stopp  of  recordings  due  to  booking  system   legality  aspects:  works  councils,  copyright  …   •  Prototype  at  HS  13  working  since  2012   •  7  more  systems  are  planned  to  start  in  autumn  2012   •  Streaming  to  lecture  halls  
  • 28. Contact   TU  Graz  –  Dept.  Social  Learning:  Team  Podcas?ng   Walther  Nagler   YpaCos  Grigoriadis     Wolfgang  Hauer   ChrisCan  SCckel     walther.nagler@tugraz.at   ypaCos@gmail.com                            Social  Learning  (TU  Graz)                          sociallearning   hZp://elearning.tugraz.at