2. Dr Archer Endrich Richard Dobson BETT January 2015, ExCeL, London ©CDP Ltd http://people.bath.ac.uk/masrwd/smcs/smcshome.html http://www.composersdesktop.com

3. "Over the past century, the UK has stopped nurturing its polymaths. You need to bring art and science back together." Eric Schmidt, MacTaggart lecture, 2012 BETT January 2015, ExCeL, London ©CDP Ltd

4. SMC is inherently multi-disciplinary BETT January 2015, ExCeL, London © CDP Ltd Music Computing Maths Physics With a long and illustrious history… Cognitive psychology, psycho-acoustics

5. Pythagoras Guido d’Arezzo Helmholtz BETT January 2015, ExCeL, London © CDP Ltd Max Mathews, 1926-2011

6. BETT January 2015, ExCeL, London © CDP Ltd Now a major academic and research subject Large international research community: http://smcnetwork.org International Conference: 2015 in Maynooth. http://www.maynoothuniversity.ie/smc15 It is now time to bring SMC into schools.

7. BETT January 2015, ExCeL, London © CDP Ltd SMC for Computer Scientists abstraction – algorithms - analysis – compression concurrency – data structures - decision-making determinism – expansion – exploration generalization – generative processes – human- computer interaction – iteration – lists – loops – maths – modules – parallelism – random – recursion repetition – selection – sequential – transformation variation …

8. BETT January 2015, ExCeL, London © CDP Ltd SMC for Musicians abstraction – algorithms - analysis – compression concurrency – data structures - decision-making determinism – expansion – exploration generalization – generative processes – human- computer interaction – iteration – lists – loops – maths – modules – parallelism – random – recursion repetition – selection – sequential – transformation variation …

9. BETT January 2015, ExCeL, London © CDP Ltd SMC for Schools? New techniques for music and MusTech students Many tools, languages for music computing (free/OS) Immediate auditory feedback (incl. for bugs!) We believe SMC offers: Engaging and effective environment for teaching CS useful approach for the visually impaired Softens boundaries between subjects

10. BETT January 2015, ExCeL, London © CDP Ltd Sound example 1 Using MIT Scratch 1.4 A classic example of musical recursion? (and concurrency / polyphony)

11. BETT January 2015, ExCeL, London © CDP Ltd Sound example 1 Using MIT Scratch 1.4 A classic example of musical recursion? (and concurrency / polyphony)

12. BETT January 2015, ExCeL, London © CDP Ltd Sound example 1 Using MIT Scratch 1.4 A classic example of musical recursion?

13. BETT January 2015, ExCeL, London © CDP Ltd Sound Example 1 Using MIT Scratch 1.4 Other classic musical “rounds” include: “Row, row, row the boat…” “Sumer is Icumen In” (mid 13 th Century) The problem with any round is – how to stop it!

14. BETT January 2015, ExCeL, London © CDP Ltd Three core aspects of SMC Algorithmic Composition Data Sonification and Audification Digital Audio (“data representation”, MIDI)

15. BETT January 2015, ExCeL, London © CDP Ltd Algorithmic Composition Richard Orton, 1940 – 2013 “All music is algorithmic”

16. BETT January 2015, ExCeL, London © CDP Ltd Algorithmic Composition Music is… “Audible mathematics” We suggest: also “audible algorithms”

17. BETT January 2015, ExCeL, London © CDP Ltd Sound Example 2 A typical approach starts with an elementary pattern:

18. BETT January 2015, ExCeL, London © CDP Ltd Sound Example 2 A typical approach starts with an elementary pattern: A plain major scale

19. BETT January 2015, ExCeL, London © CDP Ltd An Abstraction An instance

20. BETT January 2015, ExCeL, London © CDP Ltd Extensions, Variations The scale is an abstraction as is a triangle or square. It can be shifted up and down arbitrarily:

21. BETT January 2015, ExCeL, London © CDP Ltd Extensions, Variations The scale is an abstraction as is a triangle or square. It can be shifted up and down arbitrarily…... and even be drawn in a different colour! Sound Example 3

22. BETT January 2015, ExCeL, London © CDP Ltd Three levels of (finite) looping And an element of random selection – – a “generative algorithm”

23. BETT January 2015, ExCeL, London © CDP Ltd Generative Algorithms or “Generative Music” (Brian Eno): “music that is ever-different and changing, and that is created by a system” (http://en.wikipedia.org/wiki/Generative_music)http://en.wikipedia.org/wiki/Generative_music Simplest starting point – random numbers Not only for music creation – also texture, ambience, effects, foley, games

24. BETT January 2015, ExCeL, London © CDP Ltd Random Numbers No such thing as “a random number” Given a stream of N numbers, can we predict the next one? Maths: distributions, probability density functions… Computing: deterministic v stochastic, PRNG … Sound: jitter, rumble, noise (white, pink, red, brown…) Physics: Brownian motion, chaos theory…

25. BETT January 2015, ExCeL, London © CDP Ltd A Pseudo-Random Number Generator We can “randomise” anything numeric, over time: pitch duration volume instrument tempo

26. BETT January 2015, ExCeL, London © CDP Ltd Sound Example 4 varies three parameters over time:

27. BETT January 2015, ExCeL, London © CDP Ltd Sound Example 4 varies three parameters over time:

28. BETT January 2015, ExCeL, London © CDP Ltd Sound Example 5 New instrument Concurrency Random “rest” is quantised ( “sixteenth note, eighth note…”)

29. BETT January 2015, ExCeL, London © CDP Ltd Sound Example 5 New instrument(s) Concurrency Random “rest” is quantised ( “sixteenth note, eighth note…”)

30. BETT January 2015, ExCeL, London © CDP Ltd Sound Example 6 Smoothing random numbers – a simple filter Maths: calculate average, “arithmetic mean”. Computing: algorithm to compute a running sum. Each number played is the average of N random numbers. Music: what do we expect to hear?

31. BETT January 2015, ExCeL, London © CDP Ltd Sound Example 6 Smoothing random numbers – a simple filter A “virtuoso” performance…

32. BETT January 2015, ExCeL, London © CDP Ltd Sound Example 7 Two possible variations of Sound Example 6. 1. Pick out numbers above a threshold, play a long note:

33. BETT January 2015, ExCeL, London © CDP Ltd Sound Example 7 Two possible variations of Sound Example 6. 1. Pick out numbers above a threshold, play a long note:

34. BETT January 2015, ExCeL, London © CDP Ltd Sound Example 8 Two possible variations of Sound Example 6. 2. Randomise times, change the instrument:

35. BETT January 2015, ExCeL, London © CDP Ltd Sound Example 8 Two possible variations of Sound Example 6. 2. Randomise times, change the instrument: Dawn chorus on a planet far, far away?

36. BETT January 2015, ExCeL, London © CDP Ltd Sonification “the use of non-speech audio to convey information or perceptualize data” (Wikipedia) We have already heard some examples of sonification : Loops (multiply nested) Iteration Recursion (sort of…) Concurrency Random numbers Arithmetic mean

37. BETT January 2015, ExCeL, London © CDP Ltd Data Sonification mathematical functions, tables, charts images multi-dimensional data a final example of an audible algorithm – can you work out what it is doing? exploits the ability of the ear to discern information presented aurally used by e.g. NASA, CERN on large data sets

38. BETT January 2015, ExCeL, London © CDP Ltd Data Sonification mathematical functions, tables, charts images multi-dimensional data a final example of an audible algorithm – can you work out what it is doing? exploits the ability of the ear to discern information presented aurally used by e.g. NASA, CERN on large data sets