Simulating Raga Notes with a Markov Chain of Order 1-2
This is an incremental improvement for composers in computational music, specifically for generating Raga sequences.
The authors tackled algorithmic music composition for Raga Bageshree by extending a semi-natural algorithmic composition method to second-order Markov chains, finding that higher orders (three and above) are not promising due to sparse transition matrices.
Semi Natural Algorithmic composition (SNCA) is the technique of using algorithms to create music note sequences in computer with the understanding that how to render them would be decided by the composer. In our approach we are proposing an SNCA2 algorithm (extension of SNCA algorithm) with an illustrative example in Raga Bageshree. For this, Transition probability matrix (tpm) was created for the note sequences of Raga Bageshree, then first order Markov chain (using SNCA) and second order Markov chain (using SNCA2) simulations were performed for generating arbitrary sequences of notes of Raga Bageshree. The choice between first and second order Markov model, is best left to the composer who has to decide how to render these music notes sequences. We have confirmed that Markov chain of order of three and above are not promising, as the tpm of these become sparse matrices.