This document discusses how data mining can be used to analyze music and listeners. It provides examples of analyzing attributes like artist names, relationships, and fan passion. It also discusses analyzing musical attributes like loudness, timbre, and pitch to power applications like recommendations and music manipulations. The document encourages using these techniques to better understand music.

1. Data Mining Music
Paul Lamere
March 11, 2012
#SXMusicData

2. Digital music revolution continues
I’ve got 10
million
songs in my
pocket.
Now what?
#SXMusicData

3. 10 Easy Pieces
Mining Data about music Mining music itself
• Artist names
• Finding a better drummer
• Artist relationships
• The Loudness War
• Fan Passion
• Looking for the slow build
• Better recommendations
• Automatic music visualization
• Frog-based playlisting
• Data-driven music remixing
#SXMusicData

4. Tale of Two Datasets
#SXMusicData

5. MusicBrainz
• Basic metadata
• 645K Artists
• 1M Releases
• 10M Recordings
• And more ...
Let’s have some fun with MusicBrainz data
#SXMusicData

6. Have band names been getting longer?
#SXMusicData

7. Have band names been getting longer?
let’s use the some data to find out
• Collect the top 500 artists for each 5 year window
(via Echo Nest or Musicbrainz)
• Calculate the average name length for period.
#SXMusicData

8. Have artist names been getting longer?
Let’s find out with big data!
(well, really not that big)
• Collect the top 500 artists for each 5 year window
• Calculate the average name length for period.
The code is shorter than Zach’s answer
#SXMusicData

9. Have band names been getting longer?
#SXMusicData

10. Have band names been getting longer? NO
Year
Average name length
The average length of artist names peaked in the period from 1955-1959.
Average name length was 2 characters longer in 1955 than in 2012
#SXMusicData

11. Band names from 1955-1959
44 Van McCoy & The Soul City Symphony Orchestra
35 Academy of St. Martin in the Fields
33 The Clancy Brothers & Tommy Makem
33 Cliff Bennett & The Rebel Rousers
32 Maurice Williams and The Zodiacs
31 Herb Alpert & The Tijuana Brass
30 Smokey Robinson & The Miracles
30 Little Anthony & The Imperials
29 Van Morrison & The Chieftains
29 Nelson Riddle & His Orchestra
29 George Clinton and Parliament
29 Frankie Lymon & The Teenagers
29 Clancy Brothers & Tommy Makem
#SXMusicData

12. Longest band names
51 2010 Tim and Sam’s Tim and the Sam Band with Tim and Sam
48 1994 ...And You Will Know Us By The Trail Of The Dead
47 1967 Charles Wright & The Watts 103rd St Rhythm Band
46 1993 The Presidents of the United States of America
44 1959 Van McCoy & The Soul City Symphony Orchestra
43 2000 Richard Cheese & Lounge Against The Machine
43 1950 Benedictine Monks of Santo Domingo de Silos
41 1981 Emir Kusturica & The No Smoking Orchestra
39 1972 Afrika Bambaataa & The Soul Sonic Force
37 2010 Antoine Dodson & The Gregory Brothers
37 2005 Edward Sharpe and the Magnetic Zeroes
37 1999 Someone Still Loves You Boris Yeltsin
36 1977 Gloria Estefan & Miami Sound Machine
#SXMusicData

13. More fun with MusicBrainz
Artist relationship data
#SXMusicData

14. Woah!
We can build an Oracle of
Kevin Bacon for music
#SXMusicData

15. How many steps from Eric Clapton to Justin Bieber?
Eric Phil Justin
Clapton Usher
Performed Collins Bieber
Composed Who
on track
You’ll be in performed
I wish it
my Heart on First
would Rain
performed Dance with
with Phil
by Usher Justin Bieber
Collins
1 2 3
#SXMusicData

16. Six Degrees of Black Sabbath
http://labs.echonest.com/SixDegrees/?start=eric+clapton&end=justin+bieber
#SXMusicData

24. The Million Song Data Set
• Core Data:
Echo Nest analysis for a million songs
• Complimentary Data
• Second Hand Songs - 20K cover songs
• MusixMatch - 237K bucket-of-words lyric sets
• Last.fm tags - song level tags for 500K tracks.
plus 57 million sim. track pairs
• Echo Nest Taste profile subset - 1M users,
48M user/song/play count triples
http://labrosa.ee.columbia.edu/millionsong/
#SXMusicData

25. Data mining listener data
Who are the most passionate fans?
Dubstep Fan Metal heads
#SXMusicData

26. Passion Index
High plays per listener
#SXMusicData

27. Passion Index
Low plays per listener
#SXMusicData

28. Who has the most passionate fans?
#SXMusicData

29. Who has the most passionate fans?
Average 115 plays per fan
#SXMusicData

30. Passion Index
passionate
Metal heads
are passionate
music fans
#SXMusicData

31. Passion Index
Who has the least
passionate fans?
#SXMusicData

32. Passion Index Who has the least
passionate fans?
Average only 5 plays per ‘fan’
#SXMusicData

33. Better Recommendations
People who listen to the Beatles also listen to:
That’s pretty boring
#SXMusicData

34. Better Recommendations
People who bought Sgt Pepper’s also bought:
That’s pretty irrelevant
#SXMusicData

35. If you like The Beatles you might like

36. Going beyond user data
• Top ‘terms’ for an artist
• Artist popularity
• Artist familiarity
• Artist relations
• Years of activity
• Fan Passion

37. Beatles near neighbor graph tuned for discovery
The Beatles
Bob Dylan Emitt Rhodes Pink Floyd Bee Gees Elvis Presley
The Beau Brummels The Hollies Harry Nilsson Duncan Browne The Who
The Grass Roots Moby Grape The Lovin' Spoonful Badfinger The Left Banke
The Byrds Todd Rundgren The Zombies
Buffalo Springfield

38. If you like The Beatles you might like
The Beatles
Bob Dylan Emitt Rhodes Pin
The Beau Brummels The Hollies Harry Nilsson Dunc
ss Roots Moby Grape The Lovin' Spoonful Badfinger The Left Ba
The Byrds Todd Rundgren The Zombie
Buffalo Springfield

39. If you like The Beatles you might like
The Beatles
Bob Dylan Emitt Rhodes Pin
The Beau Brummels The Hollies Harry Nilsson Dunc
ss Roots Moby Grape The Lovin' Spoonful Badfinger The Left Ba
The Byrds Todd Rundgren The Zombie
Buffalo Springfield

40. More fun with artist similarity graphs
http://www.stanford.edu/~dgleich/demos/worldofmusic/WorldOfMusic.html
#SXMusicData

41. Finding a path from the
smooth jazz of Kenny G
To the brutal
death metal
of Nile
#SXMusicData

42. Finding a path from the
smooth jazz of Kenny
Kenny G
ee John Tesh Kim Waters David Sanborn David Foster Gerald Albright Dave Koz George Howard
Jan Hammer Jay Graydon Michael Kamen Stephen Bishop
Jennifer Warnes Dan Hill Andrew Gold Art Garfunkel Christopher Cross
Bread Ambrosia Benny Mardones Little River Band
P
To the brutal death metal of Nile Kenny Loggins
#SXMusicData

43. op
l Christopher Cross Dan Fogelberg Barry Manilow
Little River Band Michael McDonald Captain & Tennille
Peter Cetera Paul Carrack James Ingram Hall & Oates
Kenny Loggins Culture Club The Doobie Brothers The Righteous Brothers Phil Collins Billy
Labi Siffre Richard Marx Elton John
Rod Stewart Fleetwood Mac Paul McCartney Joe Cocker
The Rolling Stones Lindsey Buckingham Chicken Shack Peter Frampton Dance Yourself to Death
Mott The Hoople Pat Travers Band Eric Clapton Bob Seger
Dire Straits Gary Moore Jimmy Page Alvin Lee Jimi Hendrix John Mayall & The Bluesbreakers
#SXMusicData

44. Rod Stewart Fleetwood Mac Paul McCartney Joe Cocker Neil Sedaka
olling Stones Lindsey Buckingham Chicken Shack Peter Frampton Dance Yourself to Death Stevie Nicks
Mott The Hoople Pat Travers Band Eric Clapton Bob Seger
s Gary Moore Jimmy Page Alvin Lee Jimi Hendrix John Mayall & The Bluesbreakers Michael Burks
Jefferson Airplane The Doors Haale Led Zeppelin John Frusciante Janis Joplin Ace Frehley Eric Gale
Dave Navarro Beck Chad Smith The Mars Volta Slash Ataxia
Prize Fighter Inferno Sparta Free Moral Agents The Dillinger Escape Plan The Stiletto Formal Omar A. Rodrigue
Benea Reach Car Bomb Spitfire Ephel Duath Meshuggah The Number Twelve Looks Like You HORS
Synthetic Breed Zero Hour TesseracT Gojira Carcass Periphery Forbidden
Strapping Young Lad Lamb of God Nile Mastodon Septic Flesh Textures
#SXMusicData

45. Boil the Frog
#SXMusicData

46. Music is data too
#SXMusicData

47. The Beat Hierarchy
Sections
Bars
Beats
Tatums
Segments

48. #SXMusicData http://labs.echonest.com/click/

49. #SXMusicData

50. #SXMusicData

51. #SXMusicData

52. #SXMusicData

53. Fun with Loudness data
#SXMusicData

54. Why does today’s music sound like crap?
Dave Brubeck’s Take Five - released in 1959
20 dB of
dynamic
range
#SXMusicData http://musicmachinery.com/2009/03/23/the-loudness-war/

55. Why does today’s music sound like crap?
Metallica’s Cyanide - released in 2008
3 dB of
dynamic
range
#SXMusicData http://musicmachinery.com/2009/03/23/the-loudness-war/

56. The Loudness wars
#SXMusicData http://musicmachinery.com/2009/03/23/the-loudness-war/

57. Fun with Loudness Info
Looking for the slow build
#SXMusicData http://musicmachinery.com/2011/09/18/looking-for-the-slow-build/

58. Fun with Loudness Info
Looking for the slow build
#SXMusicData

59. Fun with Loudness Info
Looking for the slow build
#SXMusicData

60. Fun with Loudness Info
Looking for the slow build
#SXMusicData

61. Timbre and Pitch Data
#SXMusicData

62. Fun with Timbre info
Bohemian Rhapsichord
http://static.echonest.com/BohemianRhapsichord/index.html
#SXMusicData

63. Fun with Pitch info
MIDEM Music Machine
http://static.echonest.com/MidemMusicMachine/index.html
#SXMusicData

64. Data-driven music manipulations
Turning music into silly putty

65. Data-driven music remixing
Once you have precise information on all of the musical
events in a song, you can use this info to algorithmically
remix the song
#SXMusicData

66. Tristan’s The Swinger
Makes any song swing
#SXMusicData

67. Tristan’s Waltzify
Turns a 4/4 song into a Waltz
#SXMusicData

68. I’ve got 10 million songs in my pocket.
Now what?
Tools and
techniques that
help us better
understand the
world of music
will be
increasingly
important
#SXMusicData

69. A cautionary note
Taste is irrational
Music Music
I Like You Like
Music
I Used
To Like
#SXMusicData Get this t-shirt at dieselsweeties.com

70. More like this
http://musicmachinery.com/
#SXMusicData

71. More like this
http://blog.last.fm/category/Trends-and-Data/
#SXMusicData

72. Data Mining Music
Questions?
Paul Lamere
paul@echonest.com
@plamere
musicmachinery.com
#SXMusicData