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.

Bland Statistics

Unfortunately, the analysis tool used to create a permutation of Super Mario has ultimately led me to a dead-end. While such analytic methods might help for basic applications such as chord progressions, they clearly don't work as well when applied to individual notes, since duration, velocity, and padding must also be taken into account.

If I had the time to develop a more comprehensive analysis tool that used more than just a first-order transitional Markov matrix, I might extract better results from this analytic method. I feel, however, that this would simply take too long. Such intense analysis would merit an entire thesis all of its own.

Super Mario Statistics

Today I created a program to statistically analyze midi files and use the collected data to create a new, random 'permutation' of the file using the same statistics. I used the super mario brothers game theme song as my source midi file and the result was surprisingly interesting. Considering the simplicity of the statistics collected, I think the results demonstrate the feasability of a stylistic permutation tool as discussed in a previous post.

Shifted, Narrower Focus

I need to stop fumbling around with stuff for which I don't have a really solid goal. I need to start thinking in terms of concrete genres and concrete goals instead of trying to construct some kind of amorphous compisition from myriad modules of different production methods. I said in the beginning I wanted coherence. I've diverged a bit too much.

I'm thinking simple rock. The majority of voters in my music pole said they preferred rock. I like rock too. Plus, rock is not that hard of a genre to replicate in my opinion. Now I need to start looking at actual songs and thinking about how I would reconstruct them algorithmically, then generalize the processes.

Three Lines

Three Lines
Will define
An ardent mind's
Expense of time

They bring about
No change at all
But in and out

They are three lines.

(This poem is about the project's current code count: 9997, three lines short of a huge landmark)

Stuck

For some reason I'm just not feeling much inspiration lately. I've been pushing into the conscious model and it's a lot of work and, while the results are cool, I'm ready to hear something really inspiring. At almost ten thousand lines of code, the program is huge and I feel like I'm not getting my money's worth, so to speak. I want to hear something awesome. Guess I'll just have to keep going.

Consciousness Model Ideas

In order to create a sense of flow and retained motifs yet at the same time stylistic diversity, a generative module could at all times retain several streams of 'subconscious ideas' that represent viable solutions to the composition. Each idea uses a different set of parameter values to create an independent behavior for each idea. In this sense, each idea possesses its own personality of sorts. The module's 'conscious' thought starts on any given idea stream. The conscious thought represents the output of the module.

At any given time, however, the module may switch to another idea stream. In this way, the style changes yet the other styles (and motifs) are preserved, because the module continues to process them 'unconsciously.' Effective stream transitions could be made when two streams 'intersect,' so to speak, at a given point. In other words, if the contents of stream A at a time t are similar to stream B at the same time, consciousness may effectively change from stream A to stream B smoothly. The idea stream will then continue off in another direction, until it intersects with another stream at another point.

Here's a diagram of these ideas:
As the diagram indicates, this module has three streams in its subconscious. The streams are being processed independently (thus they have individual characteristic behaviors) but in parallel. The box of consciousness starts on a given idea stream and uses the stream to determine the module's output. The box can switch, however, to a new stream based on switch conditions (such as instantaneous stream similarity).

I think that this idea represents a step forward in both coherence and creativity. I'll try to implement it soon so I can see how the theory plays out in practice.