After reading this thread:
I’m pitching a module idea I had a while ago. A Markov Chain module in VCV to experiment with ragas (or any other style of music).
We already have VCV Library - unless games mr. chainkov but unfortunately it is bound to the 12 fixed voltages, and if I’m not mistaken only records note to next note occurrences (instead of triplets, or more), and only records pitch, leaving out glide, duration and volume.
I’d be interested in a Markov Chain module (or Kolmogorov equation based module) that we could feed song information and would build histograms out of the combinations of changes of pitch, volume, gate duration over different time lengths or a varying number of notes. Ideally it would be agnostic about pitch/volume/duration/etc. and just have a number of inputs with corresponding outputs to reproduce the character the original input, but it might be difficult with the difference in voltages and gates the way we use them in VCV.
I believe ragas would be very well captured and thus be well reproduced by a module like this because each raga style has such specific rules about the scale, note changes and rhythm.
Of course it isn’t limited to raga’s. You could feed it any song and it would reproduce a melody that has a similar character. But if music isn’t your thing you could feed it something like stock exchange data, meteoor impact data, or whatever else you’d like.
If we would add the big button functionality as is implemented in VCV Library - Stellare Modular Turing Machine I think we would have a very musical module that doesn’t create random melodies, but well informed melodies.
Of course we don’t have to limit ourselves to melodies, we could do chord progressions as well if we fed it a tuple that captures a chord.
You could even split up a single song in its structural parts: intro, verse, chorus, bridge, ending, etc., feed them into separate instances of the module and reproduce melodies from these instances in sequence to reproduce a full song with a similar feel per part.