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The Quest for Musical Genres: Do the Experts and the Wisdom of Crowds Agree?

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This paper presents some findings around musical genres. The main goal is to analyse whether there is any agreement between a group of experts and a community, when defining a set of genres and their relationships. For this purpose, three different experiments are conducted using two datasets: the MP3.com expert taxonomy, and last.fm tags at artist level. The experimental results show a clear agreement for some components of the taxonomy (Blues, HipHop), whilst in other cases (e.g. Rock) there is no correlations. Interestingly enough, the same results are found in the MIREX2007 results for audio genre classification task. Thus, showing the fact that a musical genre could have a multi–faceted definition; using expert based classifications, dynamic associations derived from the community driven annotations, and content–based analysis would improve genre classification, as well as other relevant MIR tasks such as music similarity or music recommendation.

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The Quest for Musical Genres: Do the Experts and the Wisdom of Crowds Agree?

  1. 1. ISMIR / Philadelphia, US // September, 18th 2008 The Quest for Musical Genres: Do the Experts and the Wisdom of Crowds Agree? Mohamed Sordo, Òscar Celma, Martin Blech, Enric Guaus (Music Technology Group ~ UPF)
  2. 2. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF motivation taxonomy (controlled vocabulary)
  3. 3. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF motivation taxonomy (controlled vocabulary) folksonomy (free text)
  4. 4. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF motivation taxonomy (controlled vocabulary) VS. folksonomy (free text)
  5. 5. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF expert-based • taxonomy  Mp3.com  2005
  6. 6. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF expert-based • taxonomy  13 seed genres (components)  7 levels  711 genres Rock Hip-Hop
  7. 7. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF community-based • folksonomy  last.fm 
  8. 8. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF community-based • folksonomy  last.fm  ~137K artists  ~90K tags (after cleaning)
  9. 9. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF outline 1) Mapping tags to genres 2) Computing similarity among genres 3) Agreement between experts and wisdom of crowds 4) Reconstructing the taxonomy from the folksonomy
  10. 10. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF outline 1) Mapping tags to genres 2) Computing similarity among genres 3) Agreement between experts and wisdom of crowds 4) Reconstructing the taxonomy from the folksonomy
  11. 11. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF 1) mapping tags to genres
  12. 12. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF 1) mapping tags to genres • folksonomy ~ taxonomy Jade (artist tags): 90s, illinois, new jack swing, rnb, r and b, urban, ...
  13. 13. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF 1) mapping tags to genres • folksonomy ~ taxonomy Jade (artist tags): 90s, illinois, new jack swing, rnb, r and b, urban, ...
  14. 14. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF 1) mapping tags to genres • folksonomy ~ taxonomy Jade (artist tags): 90s, illinois, new jack swing, rnb, r and b, urban, ...
  15. 15. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF 1) mapping tags to genres • folksonomy ~ taxonomy Jade (artist tags): R&B, New-Jack-Swing, Urban
  16. 16. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF 1) mapping tags to genres • folksonomy ~ taxonomy Jade (artist tags): R&B, New-Jack-Swing, Urban (39% tags matched with MP3.com genres)
  17. 17. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF outline 1) Mapping tags to genres 2) Computing similarity among genres 3) Agreement between experts and wisdom of crowds 4) Reconstructing the taxonomy from the folksonomy
  18. 18. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF 2) computing similarity among genres • Taxonomy  distance(Doo-Woop, Urban) = 3  Penalty when crossing components  distance(Urban, Rock-Pop) = 7
  19. 19. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF 2) computing similarity among genres • Folksonomy  LSA (SVD), 50 dim.  Cosine similarity  sim(Urban, Doo-Wop) = 0.868  sim(Urban, Pop-Rock) = -0.145
  20. 20. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF outline 1) Mapping tags to genres 2) Computing similarity among genres 3) Agreement between experts and wisdom of crowds 4) Reconstructing the taxonomy from the folksonomy
  21. 21. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF agreement experts ~ wisdom of crowds • 1) Separate (taxonomy) genre components using (folksonomy) genre sim.  intra-component similarity  inter-component similarity • 2) Correlation between (taxonomy) genre path distance and (folksonomy) genre sim.  DistanceTAXONOMY(g1, g2) ~???~ SimFOLKSONOMY(g1, g2)
  22. 22. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF agreement experts ~ wisdom of crowds • 1) intra-component similarity, using LSA
  23. 23. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF agreement experts ~ wisdom of crowds • 1) intra-component similarity, using LSA
  24. 24. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF agreement experts ~ wisdom of crowds • 1) intra-component similarity, using LSA Alternative-Rap Dirty-Rap West-Coast Hip-hop Bass-Music
  25. 25. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF agreement experts ~ wisdom of crowds • 1) intra-component similarity, using LSA Blues Hip-hop Rock/Pop Electronic
  26. 26. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF agreement experts ~ wisdom of crowds • 1) inter-component similarity  centroid for each component
  27. 27. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF agreement experts ~ wisdom of crowds • 1) inter-component similarity  Clearly distinguishable from the rest  Hip-hop, Blues, Jazz  Relationships found  Country ~ Bluegrass (~ Folk)  R&B-Soul ~ Gospel/Spiritual  Electronic/Dance ~ Vocal/Easy-Listening  New-Age ~ World/Reggae (!)
  28. 28. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF agreement experts ~ wisdom of crowds • 2) taxonomy genre distance vs. folksonomy genre sim. West-Coast Calypso
  29. 29. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF agreement experts ~ wisdom of crowds • 2) taxonomy genre distance vs. folksonomy genre sim.  DistanceTAXONOMY(West-Coast, Calypso) = 8 West-Coast Calypso
  30. 30. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF agreement experts ~ wisdom of crowds • 2) taxonomy genre distance vs. folksonomy genre sim.  DistanceTAXONOMY(West-Coast, Calypso) = 7  SimFOLKSONOMY(West-Coast, Calypso) = 0.04 West-Coast Calypso
  31. 31. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF agreement experts ~ wisdom of crowds • 2) taxonomy genre distance vs. folksonomy genre sim.
  32. 32. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF outline 1) Mapping tags to genres 2) Computing similarity among genres 3) Agreement between experts and wisdom of crowds 4) Reconstructing the taxonomy from the folksonomy
  33. 33. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF reconstruct taxonomy from folksonomy • Select closest parent, using folk. genre sim.  Get genres at level n
  34. 34. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF reconstruct taxonomy from folksonomy • Select closest parent, using folk. genre sim.  For each genre at level n
  35. 35. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF reconstruct taxonomy from folksonomy • Select closest parent, using folk. genre sim.  Get all nodes at level n-1 (possible parents)
  36. 36. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF reconstruct taxonomy from folksonomy • Select closest parent, using folk. genre sim.  Compute cosine LSA similarity
  37. 37. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF reconstruct taxonomy from folksonomy • Select closest parent, using folk. genre sim.  Assign closest parent
  38. 38. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF reconstruct taxonomy from folksonomy • Select closest parent, using folk. genre sim.  Compare with taxonomy parent
  39. 39. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF reconstruct taxonomy from folksonomy • Results
  40. 40. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF reconstruct taxonomy from folksonomy • Results
  41. 41. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF reconstruct taxonomy from folksonomy • Results
  42. 42. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF ...and also! • MIREX 2007 results (Team: IMIRSEL-M2K SVM) RAPHIPHOP 84.05% BLUES 77.68% EDANCE 77.68% JAZZ 72.53% COUNTRY 71.37% ROCKROLL 69.53% BAROQUE 65.81% METAL 61.11% ROMANTIC 52.79% CLASSICAL 33.33%
  43. 43. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF conclusions • Consensus in some genres  expert, community, and audio • Discovery in terms of taxonomy/folksonomy  coarse / fine grained  static / dynamic • Taxonomy adapts according to the folksonomy • Do we need experts? • Are some (wisdom-of-crowds) shepherds more experts than “THE” experts?
  44. 44. ISMIR / Philadelphia, US // September, 18th 2008 // òscar celma // MTG / UPF future work • Use more taxonomies and folksonomies • Agreement measures Uncovering affinity of artists to multiple genres from social behaviour data (Claudio Baccigalupo, Justin Donaldson, Enric Plaza)
  45. 45. ISMIR / Philadelphia, US // September, 18th 2008 THANKS!!! Mohamed Sordo, Òscar Celma, Martin Blech, Enric Guaus (Music Technology Group ~ UPF)

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