ScatologyScatology

ScatologyScatology

Study of outputStudy of output Also called Also called coprologycoprology From what comes out you get a From what comes out you get a

pretty good idea of what when in!!!!!pretty good idea of what when in!!!!!

Allusion in MusicAllusion in Music

Beethoven and MozartBeethoven and Mozart

a)

b)

Weber and BeethovenWeber and Beethovena)

b)

Stravinsky and Stravinsky and LithuaniaLithuania

a)

b)

Stravinsky and Stravinsky and Lithuania Lithuania IIII

a)

b)

Bruckner and SchubertBruckner and Schubert

a)

b)

Beethoven, Schumann, Beethoven, Schumann, Liszt, Spohr, and WagnerLiszt, Spohr, and Wagner

a)

b)

c)

d)

e)

Beethoven and Mozart IIBeethoven and Mozart II

a)

b)

Mahler and HandelMahler and Handel

a)

b)

Beethoven and HandelBeethoven and Handel

a)

b)

Various composers over Various composers over timetime

Ur-motive over 200 yearsUr-motive over 200 years

Berlioz and HaydnBerlioz and Haydn

a)

b)

Interesting tuneInteresting tune

Source Source

Chopin’s variation Chopin’s variation techniquetechnique

a)

b)( ) ( )

Algorithmic compositionAlgorithmic composition

BeethovenBeethoven

Mozart sources for algo. Mozart sources for algo. ex.ex.

Sorcerer output exampleSorcerer output example

BO1

BE1

BE2

BA1

BA2

S1

C1

BA3

BA4

What can allusions What can allusions mean?mean?

Bach’s fugue 4Bach’s fugue 4

Bach’s hidden motiveBach’s hidden motive

Mendelssohn/Wagner/Mendelssohn/Wagner/MahlerMahler

a)

b)

c)

Haydn/Beethoven/MahlerHaydn/Beethoven/Mahler

Finding musical Finding musical allusionsallusions

target work

source music

pattern match

userallusions

Intervals work bestIntervals work best

Incremental works bestIncremental works best

a)

b)

c)

d)

e)

f)

Rhythm matchingRhythm matching

a)

b)

Finding allusionsFinding allusions

Locating repeating patternsLocating repeating patterns Pattern matching a staple of Pattern matching a staple of

artificial intelligenceartificial intelligence Often called pattern recognitionOften called pattern recognition Origins in set theory in mathematicsOrigins in set theory in mathematics Finding patterns in math can be Finding patterns in math can be

quite different than finding them in quite different than finding them in music.music.

Pattern Matching codePattern Matching code

No user-given pattern No user-given pattern Segmentation (incremental)Segmentation (incremental) Controllers (variables)Controllers (variables) Too wide: noiseToo wide: noise Types of variations?Types of variations? Too narrow: no patternsToo narrow: no patterns Self-adjusting??Self-adjusting??

Types of variationsTypes of variations

Transposition Transposition Inversion Inversion Retrograde Retrograde Inversion-retrogradeInversion-retrograde Interpolated notesInterpolated notes Excised notesExcised notes Equivalent setsEquivalent sets

Set TheorySet TheoryPattern matching for Pattern matching for contemporary music. contemporary music.

Note that many musical/math Note that many musical/math set processes do not have set processes do not have

corresponding counterparts!corresponding counterparts!

Mathematical set theoryMathematical set theory

Set: {45,15,17} Set: {45,15,17} Curly bracketsCurly brackets Typically unorderedTypically unordered

Mathematical set theoryMathematical set theory

is an element of is not an element of is a proper subset of is a subset of is not a subset of the empty set; a set with no

elements union intersection

Mathematics and SetsMathematics and Sets

Example of a set proof: Example of a set proof:

A A C)C)C)C)

Venn Diagrams help!Venn Diagrams help!

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Musical set theoryMusical set theory

Set: [9,3,5]Set: [9,3,5] Brackets Brackets Ordered or unorderedOrdered or unordered Modulo 12 (pitch classes)Modulo 12 (pitch classes) Ordered version of above: [9,3,5]Ordered version of above: [9,3,5] Normal (unordered/smallest) version of Normal (unordered/smallest) version of

above [3,5,9]above [3,5,9] Prime version (unordered/invertible) of Prime version (unordered/invertible) of

above [0,2,6]above [0,2,6]

Music and SetsMusic and Sets

The same setThe same set

[0,3,7][0,3,7] [0,3,7] [0,3,7] [0,3,7] [0,3,7]

The same setThe same set

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[0,1,3,6,8,9]

Cellular automataCellular automata

Cellular automataCellular automata

An example rule setAn example rule set

8 possible ways to set upper 8 possible ways to set upper patterns (2patterns (233))

256 possible rule sets (2256 possible rule sets (288)) Follows Steven Wolfram’s model in a Follows Steven Wolfram’s model in a

New Kind of Science (NKS)New Kind of Science (NKS)

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Sequence of stepsSequence of steps

Time downward (one dimensional?) Time downward (one dimensional?) QuickTime™ and a

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Rule 30Rule 30

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Rule 90Rule 90QuickTime™ and a

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Rule 110Rule 110QuickTime™ and a

TIFF (Uncompressed) decompressorare needed to see this picture.QuickTime™ and a

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In colorIn color

Rule 30Rule 30

Rule 110Rule 110

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More about More about A New Kind of ScienceA New Kind of Science

Conway’s Game of LifeConway’s Game of Life

Conway’s Life RulesConway’s Life Rules

1.Any live cell with fewer than two live neighbors dies, as if by 1.Any live cell with fewer than two live neighbors dies, as if by loneliness.loneliness.

2.Any live cell with more than three live neighbors dies, as if by 2.Any live cell with more than three live neighbors dies, as if by overcrowding.overcrowding.

3.Any live cell with two or three live neighbors lives, unchanged, to 3.Any live cell with two or three live neighbors lives, unchanged, to the next generation.the next generation.

4.Any dead cell with exactly three live neighbors comes to life.4.Any dead cell with exactly three live neighbors comes to life.

Many different patternsMany different patterns

Gosper Glider GunGosper Glider Gun

Diehard AcornDiehard Acorn

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Game of LifeGame of Life

Many available programsMany available programs Both on site and downloadableBoth on site and downloadable Thousands of named figuresThousands of named figures Many that refigure infinitelyMany that refigure infinitely Called two dimensionalCalled two dimensional

Growth and Growth and DiminishmentDiminishment

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Genetic AlgorithmsGenetic Algorithms

Genetic AlgorithmsGenetic Algorithms Definition a computer simulationcomputer simulation in which a population of abstract

representations (called chromosomes, genotype, or genome) of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem evolves toward better solutions.

Basics A genetic representation of the solution domain, A fitness function to evaluate the solution domain.

Along the way crossover and mutation

Until a solution is found that satisfies minimum criteria

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Genotype and PhenotypeGenotype and Phenotype

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Karl SimsKarl Sims

Evolved Virtual CreaturesEvolved Virtual Creatures Not an animationNot an animation Evolved objects in motionEvolved objects in motion Encased in various media (water, Encased in various media (water,

air, etc.)air, etc.) With gravityWith gravity

Evolved Virtual Evolved Virtual CreaturesCreatures

Object Oriented Object Oriented ProgrammingProgramming

Called OOPCalled OOP Paradigm change from FP (functional Paradigm change from FP (functional

programming)programming) ClassesClasses InstancesInstances MethodsMethods InheritanceInheritance EncapsulationEncapsulation AbstractionAbstraction Polymorphism Polymorphism

GoFGoF

Gang of FourGang of Four Erich GammaErich Gamma, Richard Helm,

Ralph Johnson, and John Vlissides Design Patterns: Elements of Design Patterns: Elements of

Reusable Object-Oriented SoftwareReusable Object-Oriented Software Now in its 36th printingNow in its 36th printing 23 classic software design patterns23 classic software design patterns

CLOSCLOS

Common Lisp Object SystemCommon Lisp Object System (defclass “name” (inheritance (defclass “name” (inheritance

[superclasses])[superclasses]) (defmethod(defmethod GUI (menus, windows, buttons, etc.)GUI (menus, windows, buttons, etc.) Platform and program dependentPlatform and program dependent

Bits and PiecesBits and Pieces mapcar mapcar (mapcar #'first '((a 1)(b 2))) = (A B)

LoopLoop (loop for event in ‘((0 60 1000 1 127)(1000 62 1000 1 (loop for event in ‘((0 60 1000 1 127)(1000 62 1000 1

127))127)) collect (second event))collect (second event)) = (60 62)= (60 62) setf (simple object system)setf (simple object system) ? (setq x 'b) B ? (setf (get 'color x) 'blue) BLUE ? (get 'color x) BLUE

AssignmentAssignment

Read Chapter 4 of CMMCRead Chapter 4 of CMMC Begin work in earnest on your final Begin work in earnest on your final

projectproject Get all past homework in or else!!Get all past homework in or else!! Enjoy life, you only get so much Enjoy life, you only get so much

time.time.