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"