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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
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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.