Slides from a keynote talk at the University of Manchester UK Schools Computer Animation Competition in July 2014. http://animation14.cs.manchester.ac.uk/festival/

1. Computer Science &
Music
Sean Bechhofer
University of Manchester
!
@seanbechhofer
!
Animation 14 Awards Day,July 2014

2. Computer Science
isn't just about
Computers

3. Name That
Tune!

4. Name That
Tune!

5. Name That
Tune!

6. Name That
Tune!

7. Name That
Tune!

9. Track 1

11. Track 2

16. Cool! How
does it work?

17. How would
you describe
me?

18. Tall
48

19. 48
Scottish
Tall

20. Tall
48
Scottish
Computer Scientist

21. 48
Scottish
Computer Scientist
Guitarist
Tall

22. 48
Scottish
Computer Scientist
Guitarist
Tall
Manchester (City)

23. Tall
48
Scottish
Computer Scientist
Guitarist
Scuba Diver
Manchester (City)

24. 48
Scottish
Computer Scientist
Guitarist
Tall
Manchester (City)
Scuba Diver

25. Tall
48
Scottish
Computer Scientist
Guitarist
Scuba Diver
Features
Manchester (City)

26. These features allow
us to identify people.
We can do the same
with sound or music

27. Feature Extraction
We can analyse a piece of music to find out
what sounds are happening at different
times
!
Low frequencies: e.g.Bass
Mid frequencies: e.g.Vocals
High frequencies: e.g.Cymbals
This gives us a Spectrogram

31. Signature

33. Computer
Music Files

34. mp3?

35. “Motion Picture
Experts Group Phase
1 Audio Layer III”
Let's stick with mp3...

36. Sound = Waves

37. Turning waves into numbers
-4,0,4,7,6,3,0,-1,1,3,4,3,-1,-5,-8,-7,-5,-1,0,-2,-4,-5,-4

38. Numbers!
0000000 49 44 33 03 00 00 00 04 1b 55 54 4c 45 4e 00 00!
0000010 00 0f 40 00 01 ff fe 33 00 30 00 35 00 30 00 31!
0000020 00 33 00 41 50 49 43 00 01 02 9e 00 00 00 69 6d!
0000030 61 67 65 2f 6a 70 65 67 00 03 00 ff d8 ff e0 00!
0000040 10 4a 46 49 46 00 01 01 00 00 01 00 01 00 00 ff!
0000050 db 00 43 00 05 03 04 04 04 03 05 04 04 04 05 05!
0000060 05 06 07 0c 08 07 07 07 07 0f 0b 0b 09 0c 11 0f!
0000070 12 12 11 0f 11 11 13 16 1c 17 13 14 1a 15 11 11!
0000080 18 21 18 1a 1d 1d 1f 1f 1f 13 17 22 24 22 1e 24!
0000090 1c 1e 1f 1e ff db 00 43 01 05 05 05 07 06 07 0e!
00000a0 08 08 0e 1e 14 11 14 1e 1e 1e 1e 1e 1e 1e 1e 1e!

39. Lots of Numbers!
Over 44,000 samples per second
for high quality audio
That's 31.5 MB for a 3-minute song
Only 250 songs on an 8Gb music
player.

41. Lots of Numbers!
Over 44,000 samples per second
for high quality audio
That's 31.5 MB for a 3-minute song
Only 250 songs on an 8Gb music
player.

42. Throw some
numbers
away!

43. High Quality

44. Low Quality

45. What's in the
picture?

53. Another
volunteer!

54. Lossy Encoding
My nm s Sn nd m Cmptr Scntst t th nvrsty f
Mnchstr.
!
lk t ply th gtr.lk t g scb dvng nd shrks r my
fvrt nmls.
111 letters

55. Lossy Encoding
My name is Sean and I'm a Computer
Scientist at the University of Manchester.
!
I like to play the guitar.I like to go scuba
diving and sharks are my favourite
animals.
170 letters

56. Lossy Encoding
Original = 170 letters,New = 111 letters
!
65% smaller!
!
Imagine if we had to pay £1 per letter!
!So this is“cheaper”,but we might need
some extra effort to read it

57. Lossy Encoding
ae i ea a I a oue iei a e Uiei o aee.
I ie o a e uia.I ie o o ua ii a a ae aouie aia.
53 letters

58. Lossy Encoding
My name is Sean and I'm a Computer
Scientist at the University of Manchester.
!
I like to play the guitar.I like to go scuba
diving and sharks are my favourite
animals.
170 letters

59. Good
FastCheap
Trade Offs: Pick Any Two!

60. Everyday Encoding
r u gng to the gig?
I <3 daft punk!
not if i c u first :-)
yeah shld be gr8
me 2! c u l8r
lol ;-)

61. How high can
you hear?

62. How high?
1.Everybody stand up!
2.You will hear a tone play.
3.When you can no longer hear
the tone,sit down!
Thisisanexperiment,notacompetition—no
cheating!

63. Spot the Difference!

64. Perceptual
Encoding

65. Fewer Numbers
0000000 49 44 33 03 00 00 00 04 1b 55 54 4c 45 4e 00 00!
0000010 00 0f 40 00 01 ff fe 33 00 30 00 35 00 30 00 31!
0000020 00 33 00 41 50 49 43 00 01 02 9e 00 00 00 69 6d!
0000030 61 67 65 2f 6a 70 65 67 00 03 00 ff d8 ff e0 00!
0000040 10 4a 46 49 46 00 01 01 00 00 01 00 01 00 00 ff!
0000050 db 00 43 00 05 03 04 04 04 03 05 04 04 04 05 05!
0000060 05 06 07 0c 08 07 07 07 07 0f 0b 0b 09 0c 11 0f!
0000070 12 12 11 0f 11 11 13 16 1c 17 13 14 1a 15 11 11!
0000080 18 21 18 1a 1d 1d 1f 1f 1f 13 17 22 24 22 1e 24!
0000090 1c 1e 1f 1e ff db 00 43 01 05 05 05 07 06 07 0e!
00000a0 08 08 0e 1e 14 11 14 1e 1e 1e 1e 1e 1e 1e 1e 1e!

66. High Quality

67. mp3

70. “It's hard to make predictions,
particularly about the future.”
Niels Bohr (1885-1962)

71. Mark Zuckerberg

72. 2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
0 350 700 1050 1400
1230
1056
845
608
360
145
58
12
6
1
Million Monthly users

81. If you liked
that,you'll
like this!

82. Listens

87. Popularity
One Direction
Metallica
Daft Punk

88. Popularity
One Direction
Metallica
Daft Punk 3.4 million listeners
2.5 million listeners
0.7 million listeners

89. Popularity
One Direction
Metallica
Daft Punk
233 million listens
2.5 million listeners
173 million listens
3.4 million listeners
36 million listens
0.7 million listeners
?

90. This works for
lots of stuff!

96. Inside
&
Outside

97. Mona Lisa
Leonardo da Vinci
1503-1505
Louvre Museum

98. Who's like
me?

99. Friends?

100. Patterns

101. Patterns

102. Patterns

103. Computer Science isn't just about
Computers
We can't predict what the impact of
new technologies will be
Sharks are great!
Eat lots of broccoli!
Things to Remember

104. Thank you!
Thebestwaytopredictthefuture
istoinventit
—Alan Kay

105. Image Credits
• Daft Punk: https://www.flickr.com/photos/caesarsebastian/1031975612/
• Red Army: https://www.flickr.com/photos/patsemchism/345981835
• Constellation: http://commons.wikimedia.org/wiki/File:Ursa_major_constellation_map.png
• Great Wave: Public domain,Katsushika Hokusai ( 飾北斎)
• Etihad: http://en.wikipedia.org/wiki/File:City_Of_Manc_Stadium.jpg
• Hammerhead Shark: https://www.flickr.com/photos/barrypeters/4242623174/
• Oceanic White Tip: https://www.flickr.com/photos/michaelaston/2062803551
• Ear: https://www.flickr.com/photos/tbisaacs/3911558890/
• Fraunhofer: http://www.mp3-history.com/
• Niels Bohr: http://www.clipartsfree.net/clipart/89-niels-bohr-clipart.html
• Mark Zuckerberg: http://www.fotopedia.com/items/flickr-2326586665
• Cassette Tapes: https://www.flickr.com/photos/cass_ette/4571540478
• One Direction: http://en.wikipedia.org/wiki/File:One_Direction_at_the_Logies_Awards_2012.jpg
• Metallica: http://en.wikipedia.org/wiki/File:Metallica_London_2008-09-15_Kirk_and_James.jpg
• CD Rack: http://en.wikipedia.org/wiki/File:CD_rack.JPG
• Books: https://www.flickr.com/photos/meeli/2854849909
• Beans: http://en.wikipedia.org/wiki/File:No_name_sans_nom_beans_feves.jpg
• Video Games: http://commons.wikimedia.org/wiki/File:Videogameretaildisplay.jpg
• Broccoli: http://commons.wikimedia.org/wiki/File:Basket_of_broccoli_in_Singapore_market.jpg
• Mona Lisa: Public Domain
• Other Images: Sean Bechhofer