Computer Models of Musical Creativity

Computer Models of Musical Creativity
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"
  

These constitute the notes I took on my reading today.

Neural Networks - Emulating Human Creativity

Upon finding the article Algorithmic Composition and Reductionist Analysis: Can a Machine Compose? I immediately got excited. The author gives a great overview of algorithmic composition and details his own endeavors into the field.

In particular, the author touches on the concept of heuristic algorithms - including both genetic algorithms and neural networks - that slowly approach a desired solution by having a human evaluator determine the fitness of the system. I found the following quote astounding:

"A researcher trained a neural network to recognize makes of car from a photograph, and he decided to look inside the network at the individual neurons, rather than regarding it as a "black box" that somehow worked for some incomprehensible reason. He found that certain areas of the network were specializing into recognizing certain features of the car, and, by introducing a level of random "noise" into the network, got the network to design its own cars."

And finally, the author's take on algorithmic composition and creativity:

"I have always felt very uneasy about throwing any musical ideas away, as it would amount to destroying something that I think is unique. But, if computer composition took over to a degree, would the 'preservation people' be content with the idea that the music exists, somewhere, within the set of possibilities? May I delete Clara Empricost's symphony with impunity, once it has generated it? Should I preserve the algorithm and the random number seeds somewhere? An interesting set of problems."