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Big	
  Data	
  in	
  the	
  Music	
  Industries	
  -­‐	
  MusicDNA	
  

From	
  B2B	
  data	
  capturing	
  to	
  sophis3cated	
  B2B2C	
  Services
	
  
The	
  Norwegian	
  Council	
  of	
  Research,	
  October	
  16th,	
  2013
	
  
Dagfinn	
  Bach
	
  

the evolution of music continues
History
	
  
•  Before	
  2007	
  (founders	
  background):	
  
•  Online	
  MP3	
  scenarios	
  test-­‐cases	
  (1991-­‐1994)	
  (before	
  the	
  commercial	
  WWW)	
  
•  First	
  European	
  Music	
  Online	
  Service	
  (1995-­‐1997)	
  (6	
  countries)	
  
•  ConsulLng	
  Nokia	
  Ventures	
  (1998-­‐1999)	
  (feasibility	
  study	
  on	
  music	
  on	
  mobile)	
  
•  Music	
  aggregator	
  Artspages	
  (1999-­‐2007)	
  	
  (Today	
  Phonofile)	
  
•  From	
  2007:	
  
•  Founding	
  Bach	
  Technology	
  AS	
  in	
  Bergen	
  and	
  Bach	
  Technology	
  GmbH	
  in	
  Ilmenau	
  in	
  
the	
  building	
  of	
  Fraunhofer	
  Ins3tute	
  (2007)	
  
•  R&D	
  and	
  product	
  development	
  search/recommenda3on/metadata	
  (2007-­‐2010)	
  
•  R&D	
  and	
  product	
  development	
  audio	
  recogni3on	
  and	
  enhanced	
  players/plug-­‐ins	
  for	
  
OEM	
  products;	
  smartphone,	
  tablets	
  etc..	
  (2010-­‐2012)	
  
•  Consolida3ng	
  into	
  two	
  business	
  areas:	
  Airplay	
  monitoring	
  (Radio/TV)	
  and	
  MetaData/
BigData	
  powered	
  products	
  for	
  OEM	
  and	
  Automo3ve	
  industries	
  (in-­‐car	
  audio)	
  

2
VERDIKT	
  Project
	
  
•  “The	
  Future	
  of	
  P2P”	
  (Sustainable	
  and	
  green	
  solu3ons	
  for	
  online	
  media	
  in	
  enhanced	
  
networks).	
  	
  
•  Key	
  R&D	
  elements:	
  
•  Op3mized	
  large	
  scale	
  audio	
  recogni3on	
  
•  Audio	
  analysis	
  and	
  tagging	
  
•  Legal	
  P2P	
  solu3ons	
  with	
  automa3c	
  metadata	
  updates	
  
•  Budget:	
  16	
  MNOK	
  (4,5	
  MNOK	
  from	
  NFR)	
  
•  Partners:	
  
•  Bach	
  Technology	
  AS	
  (Bergen,	
  Norway)	
  
•  University	
  of	
  Bergen,	
  Department	
  of	
  Informa3cs	
  (Bergen,	
  Norway)	
  
•  Fraunhofer	
  Ins3tute	
  for	
  Digital	
  Media	
  Technology	
  (Ilmenau,	
  Germany)	
  
•  Other	
  contributors:	
  
•  Hewleb	
  Packard	
  Norge	
  AS	
  (HW	
  and	
  business	
  models)	
  
SERIT/Fjordane	
  IT	
  (Data	
  Centre)	
  
•  MediArena,	
  Bergen	
  (match-­‐making	
  for	
  poten3al	
  product	
  partners)	
  	
  
	
  

3
MusicDNA	
  today
	
  
MusicDNA	
  offers	
  today	
  a	
  method	
  for:	
  	
  
•  Capturing	
  audio	
  
•  Analysing	
  and	
  producing	
  metadata	
  (MusicDNA	
  descriptors)	
  
•  fingerprin3ng	
  and	
  capturing	
  more	
  data	
  
•  structuring	
  
•  storing	
  	
  
”Big	
  Data”	
  about	
  music	
  
for	
  creaIng:	
  	
  
1.  Stand-­‐alone	
  B2B	
  services	
  	
  
2.  U3lizing	
  the	
  database	
  to	
  power	
  services	
  targeted	
  for	
  end	
  consumers	
  to	
  enhance	
  the	
  
user	
  experience	
  within	
  search,	
  sharing,	
  transfer	
  and	
  visualiza3on.	
  

4
The	
  MusicDNA	
  Database
	
  
Three	
  components:	
  
1.  A	
  powerful	
  database	
  containing	
  2	
  fingerprints	
  and	
  15	
  MPEG-­‐7	
  descriptors	
  
of	
  each	
  segment	
  within	
  each	
  sound	
  tracks	
  within	
  a	
  collec3on	
  of	
  18	
  Million	
  
tracks	
  is	
  one	
  of	
  the	
  most	
  extensive	
  opera3ve	
  meta	
  databases	
  for	
  music	
  in	
  
the	
  market.	
  
2.  20.000	
  Radio	
  Channels	
  indexed	
  in	
  an	
  addi3onal	
  radio-­‐monitoring	
  database,	
  
currently	
  running	
  a	
  real-­‐3me	
  monitoring	
  of	
  4.500	
  the	
  radio	
  channels	
  across	
  
Europe,	
  and	
  another	
  1.500	
  channels	
  across	
  Canada,	
  Japan,	
  Australia.	
  
3.  Recognizing	
  audio	
  of	
  airplay	
  every	
  10	
  seconds	
  (fingerprin3ng	
  of	
  en3re	
  
track)	
  and	
  matches	
  and	
  display	
  rights	
  data	
  and	
  other	
  associated	
  data,	
  and	
  
creates	
  a	
  new	
  database	
  showing	
  the	
  history	
  of	
  airplays	
  across	
  the	
  world.	
  
All	
  databases	
  are	
  growing	
  incrementally	
  
5
MusicDNA	
  Data
	
  
•  Genre,	
  subgenre	
  
•  Tempo-­‐/Beat	
  determina3on	
  

•  Vocal	
  detec3on	
  
•  Key	
  

•  Aggressiveness	
  
•  Mood	
  

•  Synthe3city	
  
•  Rhythm	
  pabern*	
  

•  Hardness	
  

•  Vocal	
  Detec3on;	
  singer	
  type	
  (male,	
  
female,	
  child,	
  choir)*	
  
•  Vocal	
  style	
  (singing,	
  rap,	
  opera,	
  
screaming	
  etc...)*	
  
•  Cover	
  Song	
  detec3on*	
  

•  Speech-­‐/music	
  discrimina3on	
  
•  Music	
  color	
  
•  Segmenta3on	
  
•  Solo	
  Instrument	
  
•  Instrument	
  Density	
  
•  Percussiveness	
  

•  	
  +	
  4-­‐6	
  new	
  descriptors	
  every	
  year	
  
enabling	
  incrementally	
  more	
  advanced	
  
recommenda3on	
  and	
  recogni3on;	
  	
  
•  ID3	
  Data	
  
•  Soundslike	
  Fingerprint	
  
*	
  to	
  be	
  launched	
  in	
  2014	
  

6
Radio	
  Airplay	
  Data
	
  
•  For	
  each	
  airplay	
  recogni3on:	
  	
  
•  Track	
  Title	
  
•  Ar3st	
  Name	
  
•  ISRC	
  	
  (similar	
  as	
  ISBN)	
  
•  Channel	
  Name	
  
•  Country	
  of	
  Channel	
  
•  AirPlay	
  (dd.mm.yyyy),	
  3me	
  and	
  dura3on	
  (from	
  hh.mm.ss	
  to	
  hh.mm.ss)	
  
•  City	
  of	
  channel	
  loca3on	
  (including	
  GPS	
  data)	
  
•  More	
  fields	
  to	
  be	
  added	
  by	
  means	
  of	
  MusicDNA	
  tracking/matching	
  

7
Big	
  Data	
  Basis
	
  
•  18	
  million	
  tracks	
  
•  15	
  tags	
  
•  Average	
  5	
  segments	
  per	
  song	
  
•  1,35	
  Billion	
  “data	
  points”	
  for	
  describing/classifying	
  music	
  
•  Can	
  be	
  matched	
  and	
  combined	
  with	
  6	
  to	
  7	
  data	
  fields	
  for	
  Radiomonitoring	
  
•  Over	
  6.000	
  channels	
  	
  	
  -­‐>	
  soon	
  increasing	
  to	
  20.000	
  channels	
  across	
  the	
  world	
  
•  Can	
  be	
  further	
  matched	
  and	
  combined	
  with	
  data	
  from	
  affiliated	
  par3es	
  

8
Feasible	
  products	
  and	
  use	
  cases
	
  
OEM/Automo3ve	
  plug-­‐ins:	
  
•  Radio-­‐monitoring	
  for	
  iden3fica3on	
  of	
  
broadcast	
  airplays	
  
•  Radio	
  channel	
  profiler	
  (MusicDNA	
  Radio	
  
profile)	
  for	
  smart	
  radio-­‐tuner	
  apps	
  
•  Linking	
  on-­‐demand	
  music	
  to	
  radio	
  channels	
  
with	
  similar	
  profile	
  
•  “From	
  radio	
  music	
  to	
  on-­‐demand	
  music”	
  
recommenda3on	
  
•  “From	
  radio	
  to	
  radio”	
  recommenda3on	
  
Other:	
  
•  Radio-­‐plugging	
  tools	
  for	
  pre-­‐selected	
  
releases	
  
	
  
9
Feasible	
  products	
  and	
  use	
  cases
	
  
Charts	
  not	
  uIlising	
  MusicDNA	
  aSributes:	
  
•  Inter-­‐/na3onal/regional	
  (city)	
  airplay	
  charts:	
  I.E.:	
  Top	
  10,	
  20,	
  50,	
  100	
  songs	
  on	
  weekly,	
  monthly	
  
basis	
  on	
  World,	
  Europe,	
  Country,	
  City	
  level.	
  	
  
Premium:	
  
•  The	
  next	
  genera3on	
  of	
  charts:	
  real	
  3me	
  charts:	
  Top	
  10,	
  20,	
  50,	
  100	
  songs	
  constantly	
  on	
  World,	
  
Europe,	
  Country,	
  City	
  level.	
  	
  
•  specific	
  genre	
  charts	
  :	
  Combining	
  the	
  previous	
  one	
  with	
  MusicDNA	
  Abributes	
  
•  daily	
  charts:	
  last	
  day	
  (24	
  hours)	
  	
  
•  daily	
  chart	
  tendency	
  last	
  month,	
  year:	
  Visualised	
  by	
  graph	
  
•  daily	
  airplay	
  (24)	
  tendency	
  for	
  one	
  ar3st	
  during	
  one	
  month:	
  I.E:	
  visualizing	
  by	
  map	
  (one	
  per	
  day	
  
put	
  together	
  as	
  an	
  anima3on	
  of	
  30	
  days)	
  
•  day3me/	
  nigh-­‐3me	
  charts,	
  preby	
  interes3ng	
  since	
  radios	
  have	
  a	
  format	
  where	
  they	
  only	
  play	
  
interes3ng/indie	
  music	
  in	
  the	
  evening	
  or	
  at	
  night.	
  	
  Otherwise	
  similar	
  as	
  4.	
  
•  independent	
  charts:	
  Indie	
  music	
  	
  
•  newcomer	
  charts:	
  Charts	
  for	
  new	
  releases	
  (i.e.	
  last	
  week,	
  last	
  month)	
  
ConfidenIal	
  info	
  for	
  GVL	
  

10
Examples	
  on	
  charts
	
  
•  most	
  played	
  in	
  big	
  city	
  charts:	
  combining	
  with	
  popula3on	
  numbers	
  to	
  sort	
  out	
  big	
  ci3es	
  only	
  	
  
•  style/genre	
  tendency	
  in	
  different	
  countries	
  and	
  during	
  the	
  year:	
  display	
  difference	
  between	
  music	
  
profile	
  (based	
  on	
  MusicDNA)	
  in	
  different	
  countries,	
  and	
  month	
  by	
  month	
  
•  trend	
  charts:	
  showing	
  trends	
  with	
  respect	
  to	
  geographical	
  spread	
  and	
  volumes	
  for	
  one	
  ar3sts,	
  
one	
  genre,	
  etc..	
  in	
  one	
  defined	
  defined	
  territory	
  
Extended	
  with	
  genre/style	
  detecIon	
  uIlizing	
  the	
  MusicDNA	
  ASributes:	
  
•  up-­‐tempo	
  charts	
  
•  ballad	
  charts:	
  	
  
•  instrumental	
  charts	
  
•  vocal	
  charts	
  
Even	
  possible	
  to	
  make	
  further	
  extension	
  with	
  the	
  following	
  MusicDNA	
  aSributes:	
  
•  dark/bright	
  
•  hard/som	
  
•  full/sparse	
  (size	
  of	
  the	
  ensemble)	
  
Significant	
  potenIal	
  for	
  VizualizaIon	
  
•  	
  
	
  

ConfidenIal	
  info	
  for	
  GVL	
  

	
  

11
Demos
	
  
•  MusicDNA	
  Radio	
  monitor:	
  
•  Ar3st	
  Centric	
  (see	
  abached	
  screen-­‐dump)	
  
•  Chart	
  	
  (see	
  abached	
  screen-­‐dump)	
  
•  Vizualisa3on	
  ideas:	
  
•  Ylvis	
  Map	
  (from	
  screen	
  shots)	
  
•  Vizrt	
  vizualisa3on	
  sketches:	
  
•  Ylvis	
  
•  David	
  Gueba	
  
•  Emmelie	
  de	
  Forrest	
  (Eurovision)	
  1	
  
•  Emmelie	
  de	
  Forrest	
  (Eurovision)	
  2	
  

12
Social	
  media	
  scenarios	
  (in	
  progress)
	
  
A:	
  IntegraIng	
  Apply	
  Magic	
  Sauce	
  with	
  the	
  MusicDNA	
  mobile	
  player	
  
•  Descrip3on:	
  	
  
•  Giving	
  users	
  the	
  op3on	
  to	
  connect	
  with	
  facebook,	
  get	
  their	
  personality	
  score	
  
instantly,	
  see	
  which	
  performing	
  ar3sts	
  in	
  their	
  library	
  have	
  a	
  similar	
  psychological	
  
profile,	
  see	
  links	
  to	
  discover	
  music	
  or	
  purchase	
  3ckets	
  for	
  other	
  ar3sts	
  which	
  they	
  
may	
  not	
  know	
  about	
  but	
  which	
  also	
  share	
  their	
  profile.	
  Users	
  could	
  also	
  opt	
  in	
  to	
  
submit	
  their	
  data	
  anonymously	
  for	
  academic	
  research.	
  
•  Usage	
  of	
  data:	
  
•  Process	
  the	
  personal	
  informa3on	
  and	
  aggregate	
  it	
  before	
  sending	
  the	
  analy3cs	
  on	
  
the	
  personality,	
  IQ,	
  life	
  sa3sfac3on,	
  etc.	
  of	
  the	
  users	
  who	
  connected	
  and	
  use	
  the	
  
player.	
  We	
  can	
  then	
  use	
  these	
  insights	
  in	
  any	
  way	
  you	
  find	
  useful	
  informa3on,	
  
whether	
  to	
  understand	
  the	
  users	
  beber,	
  i.e.	
  for	
  UI	
  personalisa3on,	
  or	
  presen3ng	
  this	
  
informa3on	
  to	
  clients	
  

13
Social	
  media	
  scenarios	
  (in	
  progress)
	
  
B:	
  AddiIonal	
  dimensions	
  in	
  the	
  music	
  profiler	
  and	
  recommender	
  
•  Descrip3on:	
  	
  
•  A	
  huge	
  poten3al	
  to	
  add	
  an	
  addi3onal	
  personality	
  level	
  to	
  the	
  exis3ng	
  profiler	
  and	
  
recommenda3on	
  plaporm.	
  We	
  could	
  analyse	
  the	
  profiles	
  and	
  listening	
  stats	
  of	
  
different	
  radio	
  channels	
  and	
  online	
  plaporms	
  such	
  as	
  Mixcloud,	
  Soundcloud	
  and	
  
Last.fm	
  to	
  target	
  recommenda3ons	
  more	
  accurately.	
  Combined	
  with	
  MusicDNA	
  this	
  
informa3on	
  could	
  be	
  presented	
  not	
  only	
  as	
  channels	
  or	
  songs	
  that	
  the	
  user	
  would	
  
like,	
  but	
  as	
  a	
  MusicDNA+personality	
  profile	
  of	
  a	
  user's	
  en3re	
  collec3on,	
  which	
  they	
  
have	
  the	
  op3on	
  to	
  rec3fy	
  and	
  thus	
  tell	
  you	
  even	
  more	
  about	
  the	
  kind	
  of	
  music	
  they	
  
want	
  to	
  listen	
  to.	
  
•  Usage	
  of	
  data:	
  
•  It	
  would	
  then	
  be	
  very	
  easy	
  to	
  use	
  this	
  informa3on	
  to	
  suggest	
  concert	
  3ckets,	
  
merchandise	
  and	
  other	
  products	
  to	
  the	
  user	
  as	
  we	
  would	
  have	
  a	
  far	
  more	
  detailed	
  
understanding	
  of	
  what	
  they	
  are	
  likely	
  to	
  purchase	
  or	
  which	
  gig	
  they	
  are	
  likely	
  to	
  
abend	
  
14
Thank	
  You!
	
  

www.musicdna.com
	
  
dagfinnb@musicdna.com
	
  

the evolution of music continues
ArIst	
  Centric	
  Radio	
  Monitor	
  	
  -­‐	
  	
  Screen-­‐dump
	
  

16
Chart	
  Radio	
  Monitor	
  	
  -­‐	
  	
  Screen-­‐dump
	
  

17

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Big Data in the Music Industries, Dagfinn Bach, Bach Technology

  • 1. Big  Data  in  the  Music  Industries  -­‐  MusicDNA   From  B2B  data  capturing  to  sophis3cated  B2B2C  Services   The  Norwegian  Council  of  Research,  October  16th,  2013   Dagfinn  Bach   the evolution of music continues
  • 2. History   •  Before  2007  (founders  background):   •  Online  MP3  scenarios  test-­‐cases  (1991-­‐1994)  (before  the  commercial  WWW)   •  First  European  Music  Online  Service  (1995-­‐1997)  (6  countries)   •  ConsulLng  Nokia  Ventures  (1998-­‐1999)  (feasibility  study  on  music  on  mobile)   •  Music  aggregator  Artspages  (1999-­‐2007)    (Today  Phonofile)   •  From  2007:   •  Founding  Bach  Technology  AS  in  Bergen  and  Bach  Technology  GmbH  in  Ilmenau  in   the  building  of  Fraunhofer  Ins3tute  (2007)   •  R&D  and  product  development  search/recommenda3on/metadata  (2007-­‐2010)   •  R&D  and  product  development  audio  recogni3on  and  enhanced  players/plug-­‐ins  for   OEM  products;  smartphone,  tablets  etc..  (2010-­‐2012)   •  Consolida3ng  into  two  business  areas:  Airplay  monitoring  (Radio/TV)  and  MetaData/ BigData  powered  products  for  OEM  and  Automo3ve  industries  (in-­‐car  audio)   2
  • 3. VERDIKT  Project   •  “The  Future  of  P2P”  (Sustainable  and  green  solu3ons  for  online  media  in  enhanced   networks).     •  Key  R&D  elements:   •  Op3mized  large  scale  audio  recogni3on   •  Audio  analysis  and  tagging   •  Legal  P2P  solu3ons  with  automa3c  metadata  updates   •  Budget:  16  MNOK  (4,5  MNOK  from  NFR)   •  Partners:   •  Bach  Technology  AS  (Bergen,  Norway)   •  University  of  Bergen,  Department  of  Informa3cs  (Bergen,  Norway)   •  Fraunhofer  Ins3tute  for  Digital  Media  Technology  (Ilmenau,  Germany)   •  Other  contributors:   •  Hewleb  Packard  Norge  AS  (HW  and  business  models)   SERIT/Fjordane  IT  (Data  Centre)   •  MediArena,  Bergen  (match-­‐making  for  poten3al  product  partners)       3
  • 4. MusicDNA  today   MusicDNA  offers  today  a  method  for:     •  Capturing  audio   •  Analysing  and  producing  metadata  (MusicDNA  descriptors)   •  fingerprin3ng  and  capturing  more  data   •  structuring   •  storing     ”Big  Data”  about  music   for  creaIng:     1.  Stand-­‐alone  B2B  services     2.  U3lizing  the  database  to  power  services  targeted  for  end  consumers  to  enhance  the   user  experience  within  search,  sharing,  transfer  and  visualiza3on.   4
  • 5. The  MusicDNA  Database   Three  components:   1.  A  powerful  database  containing  2  fingerprints  and  15  MPEG-­‐7  descriptors   of  each  segment  within  each  sound  tracks  within  a  collec3on  of  18  Million   tracks  is  one  of  the  most  extensive  opera3ve  meta  databases  for  music  in   the  market.   2.  20.000  Radio  Channels  indexed  in  an  addi3onal  radio-­‐monitoring  database,   currently  running  a  real-­‐3me  monitoring  of  4.500  the  radio  channels  across   Europe,  and  another  1.500  channels  across  Canada,  Japan,  Australia.   3.  Recognizing  audio  of  airplay  every  10  seconds  (fingerprin3ng  of  en3re   track)  and  matches  and  display  rights  data  and  other  associated  data,  and   creates  a  new  database  showing  the  history  of  airplays  across  the  world.   All  databases  are  growing  incrementally   5
  • 6. MusicDNA  Data   •  Genre,  subgenre   •  Tempo-­‐/Beat  determina3on   •  Vocal  detec3on   •  Key   •  Aggressiveness   •  Mood   •  Synthe3city   •  Rhythm  pabern*   •  Hardness   •  Vocal  Detec3on;  singer  type  (male,   female,  child,  choir)*   •  Vocal  style  (singing,  rap,  opera,   screaming  etc...)*   •  Cover  Song  detec3on*   •  Speech-­‐/music  discrimina3on   •  Music  color   •  Segmenta3on   •  Solo  Instrument   •  Instrument  Density   •  Percussiveness   •   +  4-­‐6  new  descriptors  every  year   enabling  incrementally  more  advanced   recommenda3on  and  recogni3on;     •  ID3  Data   •  Soundslike  Fingerprint   *  to  be  launched  in  2014   6
  • 7. Radio  Airplay  Data   •  For  each  airplay  recogni3on:     •  Track  Title   •  Ar3st  Name   •  ISRC    (similar  as  ISBN)   •  Channel  Name   •  Country  of  Channel   •  AirPlay  (dd.mm.yyyy),  3me  and  dura3on  (from  hh.mm.ss  to  hh.mm.ss)   •  City  of  channel  loca3on  (including  GPS  data)   •  More  fields  to  be  added  by  means  of  MusicDNA  tracking/matching   7
  • 8. Big  Data  Basis   •  18  million  tracks   •  15  tags   •  Average  5  segments  per  song   •  1,35  Billion  “data  points”  for  describing/classifying  music   •  Can  be  matched  and  combined  with  6  to  7  data  fields  for  Radiomonitoring   •  Over  6.000  channels      -­‐>  soon  increasing  to  20.000  channels  across  the  world   •  Can  be  further  matched  and  combined  with  data  from  affiliated  par3es   8
  • 9. Feasible  products  and  use  cases   OEM/Automo3ve  plug-­‐ins:   •  Radio-­‐monitoring  for  iden3fica3on  of   broadcast  airplays   •  Radio  channel  profiler  (MusicDNA  Radio   profile)  for  smart  radio-­‐tuner  apps   •  Linking  on-­‐demand  music  to  radio  channels   with  similar  profile   •  “From  radio  music  to  on-­‐demand  music”   recommenda3on   •  “From  radio  to  radio”  recommenda3on   Other:   •  Radio-­‐plugging  tools  for  pre-­‐selected   releases     9
  • 10. Feasible  products  and  use  cases   Charts  not  uIlising  MusicDNA  aSributes:   •  Inter-­‐/na3onal/regional  (city)  airplay  charts:  I.E.:  Top  10,  20,  50,  100  songs  on  weekly,  monthly   basis  on  World,  Europe,  Country,  City  level.     Premium:   •  The  next  genera3on  of  charts:  real  3me  charts:  Top  10,  20,  50,  100  songs  constantly  on  World,   Europe,  Country,  City  level.     •  specific  genre  charts  :  Combining  the  previous  one  with  MusicDNA  Abributes   •  daily  charts:  last  day  (24  hours)     •  daily  chart  tendency  last  month,  year:  Visualised  by  graph   •  daily  airplay  (24)  tendency  for  one  ar3st  during  one  month:  I.E:  visualizing  by  map  (one  per  day   put  together  as  an  anima3on  of  30  days)   •  day3me/  nigh-­‐3me  charts,  preby  interes3ng  since  radios  have  a  format  where  they  only  play   interes3ng/indie  music  in  the  evening  or  at  night.    Otherwise  similar  as  4.   •  independent  charts:  Indie  music     •  newcomer  charts:  Charts  for  new  releases  (i.e.  last  week,  last  month)   ConfidenIal  info  for  GVL   10
  • 11. Examples  on  charts   •  most  played  in  big  city  charts:  combining  with  popula3on  numbers  to  sort  out  big  ci3es  only     •  style/genre  tendency  in  different  countries  and  during  the  year:  display  difference  between  music   profile  (based  on  MusicDNA)  in  different  countries,  and  month  by  month   •  trend  charts:  showing  trends  with  respect  to  geographical  spread  and  volumes  for  one  ar3sts,   one  genre,  etc..  in  one  defined  defined  territory   Extended  with  genre/style  detecIon  uIlizing  the  MusicDNA  ASributes:   •  up-­‐tempo  charts   •  ballad  charts:     •  instrumental  charts   •  vocal  charts   Even  possible  to  make  further  extension  with  the  following  MusicDNA  aSributes:   •  dark/bright   •  hard/som   •  full/sparse  (size  of  the  ensemble)   Significant  potenIal  for  VizualizaIon   •      ConfidenIal  info  for  GVL     11
  • 12. Demos   •  MusicDNA  Radio  monitor:   •  Ar3st  Centric  (see  abached  screen-­‐dump)   •  Chart    (see  abached  screen-­‐dump)   •  Vizualisa3on  ideas:   •  Ylvis  Map  (from  screen  shots)   •  Vizrt  vizualisa3on  sketches:   •  Ylvis   •  David  Gueba   •  Emmelie  de  Forrest  (Eurovision)  1   •  Emmelie  de  Forrest  (Eurovision)  2   12
  • 13. Social  media  scenarios  (in  progress)   A:  IntegraIng  Apply  Magic  Sauce  with  the  MusicDNA  mobile  player   •  Descrip3on:     •  Giving  users  the  op3on  to  connect  with  facebook,  get  their  personality  score   instantly,  see  which  performing  ar3sts  in  their  library  have  a  similar  psychological   profile,  see  links  to  discover  music  or  purchase  3ckets  for  other  ar3sts  which  they   may  not  know  about  but  which  also  share  their  profile.  Users  could  also  opt  in  to   submit  their  data  anonymously  for  academic  research.   •  Usage  of  data:   •  Process  the  personal  informa3on  and  aggregate  it  before  sending  the  analy3cs  on   the  personality,  IQ,  life  sa3sfac3on,  etc.  of  the  users  who  connected  and  use  the   player.  We  can  then  use  these  insights  in  any  way  you  find  useful  informa3on,   whether  to  understand  the  users  beber,  i.e.  for  UI  personalisa3on,  or  presen3ng  this   informa3on  to  clients   13
  • 14. Social  media  scenarios  (in  progress)   B:  AddiIonal  dimensions  in  the  music  profiler  and  recommender   •  Descrip3on:     •  A  huge  poten3al  to  add  an  addi3onal  personality  level  to  the  exis3ng  profiler  and   recommenda3on  plaporm.  We  could  analyse  the  profiles  and  listening  stats  of   different  radio  channels  and  online  plaporms  such  as  Mixcloud,  Soundcloud  and   Last.fm  to  target  recommenda3ons  more  accurately.  Combined  with  MusicDNA  this   informa3on  could  be  presented  not  only  as  channels  or  songs  that  the  user  would   like,  but  as  a  MusicDNA+personality  profile  of  a  user's  en3re  collec3on,  which  they   have  the  op3on  to  rec3fy  and  thus  tell  you  even  more  about  the  kind  of  music  they   want  to  listen  to.   •  Usage  of  data:   •  It  would  then  be  very  easy  to  use  this  informa3on  to  suggest  concert  3ckets,   merchandise  and  other  products  to  the  user  as  we  would  have  a  far  more  detailed   understanding  of  what  they  are  likely  to  purchase  or  which  gig  they  are  likely  to   abend   14
  • 15. Thank  You!   www.musicdna.com   dagfinnb@musicdna.com   the evolution of music continues
  • 16. ArIst  Centric  Radio  Monitor    -­‐    Screen-­‐dump   16
  • 17. Chart  Radio  Monitor    -­‐    Screen-­‐dump   17