Posts In: Sources & Notes

Project a New Reality: Reading List 1

March 21, 2010 Sources & Notes 0 Comments

Creating an entire algorithmic reality is going to take a lot of research and work in a vast number of fields. I've purchased the following books and am absorbing them with my dream of A New Reality in mind.

My primary focus, at the moment, other than algorithmic music, is algorithmic terrains (also known as procedural terrains).  I am exploring planetary geomorphology, the physical side of the field, as well as advanced terrain algorithms for implementing realistic techniques in code form.  I am also highly interested in star systems, galaxies, and cosmic features in general.  Nothing makes a beautiful reality like the vastness of outer-space.  On top of all that, I need the background knowledge in Direct3D and OpenGL to be able to bring everything to life.  Since I haven't decided which route to take on d3d vs. GL just yet, I figure I'll go ahead and learn both of them.  The more knowledge, the better.

I've also thrown in a random book about trees, just because I love trees and want to be able to algorithmically create them.  Having mulled the processes over in my head for a while and had several good ideas, I figure some nice pictures will really inspire me (though I still don't have quite enough background in 3D engines to implement the complexities just yet).

Color code

  • Finished
  • In progress
  • Not yet started

Notes from Talk with Peter Torpey at MIT

October 16, 2009 Sources & Notes 0 Comments
  • EchoNest is doing the "inverse" of what I'm doing
  • Could use "cloud" processing to make small generative music devices possible (instead of having an on-board rendering engine, send rendering parameters to a server and render the compositions on a large, specialized parallel processing network at the server.)
  • Though there are not dedicated algorithmic composition projects going on at the Media Lab right now, it's definitely something that would fit in

More to come later when I remember the rest of the stuff we talked about!

Rhythm and Meter

As the foundations upon which music is built, rhythm and meter will play an obvious and pivotal role in my program. Unfortunately, I have read very little on the topics, as Music, the Brain, and Ecstasy devoted only a single chapter to the subject in general. I need to delve deeper into the topic. To do so, I'll need some good sources.

Here are some books I'm looking at:

The first one looks extremely comprehensive and helpful.

The Musicality of Language

The Musicality of Language

A very interesting article that touches on some rhythmic similarities between language and music.

Computer Models of Musical Creativity (4)

Computer Models of Musical Creativity (David Cope)
Chapter 4: Recombinance

  • Western tonal music generally follows simple principles that drive melody, harmony, voice leading, and hierarchical form
  • One can create music by programming such principles into a computer
  • Such an approach often creates stale music
  • Recombinance is a method of using existing music and recombining it logically to create new music
  • Cope uses destination pitches and beat-size groupings to split chorales into smaller groups called lexicons that can be recombined using the pitch and beat data
  • Such syntactic networking actually preserves a great deal of the music's integrity while generating new output
  • To further extend the abilities of recombinance, Cope had his program analyze the source piece's "distance to cadence, position of groupings in relation to meter, and other context-sensitive features"
  • Artists often use musical signatures, patterns of notes that recur in many works of a composer
  • Recombinance can be described in terms of Markov chains
  • Recombinance can work both vertically and horizontally
  • Generation of music must start with an abstract hierarchy and move towards specifics (this is exactly what I foresaw and intended when I made the structure module the foundation upon which mGen works! Cope agrees!)
  • Rule acquisition from music models the musical training of humans
  • Machine renditions of music are often crude and dead...successful algorithmic composition requires dynamics
  • An improviser basically has a repertory and an idea of how he or she wants an improvised idea to flow into the next
  • "Recombinance, or rules acquisition, provides more logical and successful approaches to composing in tonal music styles"
  • "Every work of music, I feel, contains a set of instructions for creating different but highly related replications of itself"
  • "The secret of successful creativity lies not in the invention of new alphabet letters or musical pitches, but in the elegance of the combination and recombination of existing letters and pitches"
  • "In recombination, rules are not necessary, since the destination notes provide all of the requisite information"
  • "While recombinance of this type ensures beat-to-beat logic in new compositions, it does not guarantee the same logic at higher levels"
  • "The initial and final groupings of a phrase are most pivotal"
  • "Experiments in Musical Intelligence protects signatures from being fragmented into smaller groupings, thus ensuring that these signatures will survive the recombination process"
  • "A Markovian description of recombinant processes does not allow for the broader control of larger-scale structure"
  • "In music, what happens in measure 5 may directly influence what happens in measure 55, without necessarily affecting any of the intervening measures"
  • "The top-down approach is necessary because choosing new beat-to-beat groupings must be informed by hierarchy, and not the reverse. No new grouping of a work-in-progress can be selected until its implications for the entire structure of the work are determined"
  • "Acquired rules are often more accurate since, by default, they originate from the music itself and not from generalizations about the music"
  • "Having a program first derive rules and then apply these rules during composition, though a simple notion, is critically important to the basic thrust of my modeling creativity"
  • "I continue to maintain that computer-composed music in any style is as real as human-composed music in any style"
  • "I see no reason why computer-created music cannot move us to tears, find roots in our cultures, and reveal or obscure its internal implications as much as any music composed in more traditional ways"
  • "Improvisation consists of either generating music associatively to maintain continuity, or interruptively striking out in apparently new directions"
  • "Improvisers associate rhythmic patterns, melodic contours, and harmony"
  • "Improvisation tends to function as a series of gestures that themselves have a sense of beat and that, when performed one after another, make musical, rhythmic, and metric sense"

Computer Models of Musical Creativity (3)

March 30, 2009 Sources & Notes 0 Comments

Computer Models of Musical Creativity (David Cope)
Chapter 3: Current Models of Musical Creativity

  • Although randomness often competes with creativity in terms of surprise, it is no substitute for a creative process
  • Most random processes are simply too complex to predict
  • Randomness arises from a lack of predictability using logic, not a lack of determinism
  • Good creativity simply requires good algorithms
  • Some models of creativity include cellular automata, mathematical models, fuzzy logic, neural networks, and Markov chains
  • Using Markov chains, one can analyze a piece of music and produce new music in roughly the same style
  • Genetic algorithms operate on the principle of natural selection
  • Genetic algorithms and cellular automata can both generate very complex output
  • In rule-based programs, creativity really belongs to the programmer, not the program
  • Neural networks use "hidden unit" networks to simulate the output of a given situation based on a sample input and output
  • Neural networks simulate the workings of the human brain
  • Mathematical formulas can be used to produce quasi randomness
  • "Randomness is not an engaging mystery, but a simple reflection of ignorance"
  • "Randomness refers to behavior that is either too complex, too patternless, or too irrelevant to make prediction possible"
  • "For those believing that using algorithms to create music somehow removes imagination, inspiration, and intuition from the composing process, know that defining a good algorithm requires as much imagination, inspiration, and intuition as does composing a good melody or harmony"
  • "Neither good algorithms nor good musical ideas grow on trees"
  • "Integrating association-based procedures with data-driven processes increases the creative potential of this approach to music composition"
  • "GAs typically involve DNA-like inheritance of characteristics as well as crossover and mutation techniques to develop new traits"
  • "Neural networks then cycle through a series of forward or back propagations that compare output with input and alter hidden unit values, until the output values match or approximate the relationships of the input and output data upon which they were trained"
  • "Sandwiched between these nodes are variable numbers of layers of hidden units, as well as variable numbers of connections between these inputs, outputs, and hidden units, making the training process extremely complex"
  • "We should not overestimate the abilities of neural networks or let comtivity mask a lack of true creativity"
  • "Mathematical origins for algorithmic music, while occasionally producing interesting results, in no way indicate the presence of creativity"
  • "Computer programs must be sufficiently independent of their programmers and users in order to qualify as truly creative. Most apparently creative algorithmic composing programs either produce enormous output from which users make preferential choices or invoke so many programmer-defined rules that the software only proves the creativity of the programmer"

Computer Models of Musical Creativity (2)

March 11, 2009 Sources & Notes 0 Comments

Computer Models of Musical Creativity (David Cope)
Chapter 2: Background

  • Surprise is a necessary ingredient in creativity
  • Associationism - a view that creativity stems of unconscious or subconscious associations - linking thoughts, ideas, experiences, and images
  • Letter Spirit, a program used to creatively make new fonts, operates withdecision waves - unifying operations that consist of "the gradual, slow-but-sure tightening up of internal consistency all across the structure under construction"
  • Cope feels that focusing on details is not the important part of a creative work
  • The Turing test may also be an adequate gauge of creativity
  • Creativity is not an output, rather, it is a function
  • Creativity is not necessarily audible - it is only perceptible when the method of creation is known
  • "Most recent research in artificial intelligence and creativity tends to focus on connectionism, geneticism, and expertism"
  • "Full-scale creativity consists in having a keen sense for what is interesting, following it recursively, applying it at the meta-level, and modifying it accordingly" - Hofstadter
  • "I do not believe ... that all humans share a common aesthetic"
  • "It is the processes, rather than their output, that best identify creativity"
  • "The results of creativity should be different from the results of other processes"
  • "Creativity is a process, not the result of a process"

Computer Models of Musical Creativity (1)

March 10, 2009 Sources & Notes 0 Comments

Computer Models of Musical Creativity (David Cope)
Chapter 1: Definitions

  • Many see creativity as restricted to humans
  • If humans can't create creative machines, then are they really creative in the first place?
  • Two important questions when considering creativity: must the creator know what he or she is creating and must they appreciate his or her own creation?
  • Creativity is directly related to intelligence
  • Intelligence consists of the following abilities: to learn, to remember, to infer, to analogize, and to create
  • Combinatorial accidents can be powerful creative tools and also produce striking originality
  • "Originality must be useful"
  • "Cross-wiring can produce interesting, unique, and important creativeconnections"
  • "Vertically thinking is selective and analytical, while lateral thinking is generative and instigative"
  • Cope's definition of creativity: "The initialization of connections between two or more multifaceted things, ideas, or phenomena hitherto not otherwise considered actively connected"
  • "I do not feel that creativity requires intelligence, nor does it consequently require life"
  • "Creativity should never be confused with arbitrary or convenient contrivances that simply take any road untried for the sake of novelty"

Music, the Brain, and Ecstasy (6)

Music, the Brain, and Ecstasy (Robert Jourdain)
Chapter 6: Composition

  • Imagery describes what perception cannot
  • The brain always works through abstract hierarchies, connecting ideas with a web of relations rather than storing a single data bank for a specific subject
  • Composers' brains group notes, chords, and progressions with such relations
  • [My own deduction] "Categorization circuitry" is essentially what is needed to build a computer capable of composition
  • A soloist draws upon a vocabulary of sounds a phrases that comprise a musical language
  • In terms of improvisation, a brain can't generate complex, far-reaching structures enough to improvise a piece of significant depth [but perhaps a computer can?]
  • Mozart mapped out the structure of his compositions before filling in the details (this is great news, because it's how my program is designed! So at least I know this method is workable!)
  • Scores can limit the capacity of composers (this again is good news: my program is not bounded by traditional scoring)
  • Intelligence comes from neural networks
  • "It is memory that is the composer's workshop"
  • "The phenomenon of musical ideas arriving full-blown in the composer's mind is called inspiration"
  • "[The soloist] works from a vocabulary of sounds, and a kind of grammar for juxtaposing them, that has become his musical language"
  • "Musicians work through a hierarchy of ready-made movements. Thousands of patterns of scales and arpeggios and chord progressions are deeply channeled in their nervous system"
  • "[Mozart] began by mapping a composition's structure. Only later did he go back to fill in supporting voices and embellishments that could be written in any number of ways without changing the basic character of the piece"
  • "Music notation tends to discourage complex melodies and rhythms"
  • "By promoting abstracted, hierarchical thinking, the score can seduce composers into a theoretical, unmusical approach to composition"
  • "In the end, intellectual power of any kind arises from the laborious creation of networks of neurons"

Music, the Brain, and Ecstasy (5)

Music, the Brain, and Ecstasy (Robert Jourdain)
Chapter 5: Rhythm

  • Meter vs. Phrasing
  • Phrasing is a higher-level unit 0f perception than meter, and encompasses harmonic tension, contour, and dynamics
  • Pulse lies at the core of meter
  • Perception of meter is based on prime numbers
  • Polyrhythms are made by playing more than one meter at a time
  • Syncopation is created when beats are accentuated apart from the regular metrical pattern; often the offbeats are regular enough to anticipate
  • Memory vs. Anticipation
  • Memory recalls what has already happened, anticipation draws on memory to predict notes to come (usually only a beat or two in the future)
  • Importance of tempo: if music moves too slowly, the relations are not close enough to be intelligible; if music moves too quickly, the brain cannot keep up with the relation modeling and has to move to shallower relations, missing the nuances of the piece
  • Music needs some gradual changes in tempo; sounds unnatural without them
  • Harmonic complexity vs. Metrical Complexity have an inverse relationship
  • Most listeners and composers alike now opt for harmonic complexity since harmony information is parallel, while metric information is serial, thus more harmony information can be modeled in a shorter time
  • "Memory is music's canvas"
  • "In music, it is phrasing that reaches farthest across time to encompass the deepest relations"
  • "Composers gain maximum effect by interweaving the tensions created by music's various aspects"
  • "Tempo matters because the mechanics of music perception are exceedingly sensitive to the rate at which musical structures are presented to the brain"
  • "Most tempo fluctuations are made intentionally. Music just doesn't sound right without them"
  • "The more harmony wanders from its tonal center, the more it requires rhythmic buttressing"