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Automated Podcasting System for Universities

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Automated Podcasting System for Universities

  1. 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. 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. 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  
  4. 4. Recording  Services  -­‐  Overall  
  5. 5. Recording  Services  –  General  Recording  
  6. 6. Recording  Services  –  Streaming  I  
  7. 7. Recording  Services  –  Streaming  II   hEp://curry.tugraz.at  
  8. 8. Recording  Services  -­‐  Devices  
  9. 9. Strategy:  Open  EducaConal  Resources   hEp://opencontent.tugraz.at   Model  by  Schaffert  (Schaffert,  2010)  adapted  to  TU  Graz  IniCaCves  
  10. 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. 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. 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. 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. 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. 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. 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. 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. 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. 19. Searchable  Recordings  by  Indexing  Screencasts  V  
  20. 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. 21. Searchable  Recordings  by  Indexing  Screencasts  VII   Method   implemented  in   summer  2011   SCll  under  further   development  
  22. 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      
  23. 23. Automated  Recording  I  
  24. 24. Automated  Recording  II  
  25. 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  
  26. 26. Automated  Recording  IV  
  27. 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. 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    

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