Gaayaka | S, N D | N S R G | ((P S,,)) , ((S , S>>> S)) -((D. S. D)) ((S , S>> S))- S R ((G<< G , ,)) Subramanian, 2009 Database of phrases Automatic Gamakam feature – guided
Tonic note identification Bellur, A., V. Ishwar, X. Serra, and H. A. Murthy (2012) A knowledge based signal processing approach to tonic identification in indian classical music. Bellur, A., and H. A. Murthy (2013) Automatic tonic identification in classical music using melodic characteristics and tuning of the drone. Srihari, S. (2016) * Pick the most frequent note, it mostly just works. * not really, no
Melody insights #1 Tonic note is prominent Sa, and Pa have higher and sharper peaks Other note peaks are blunt Probabilities of all swarams in a raga are not the same Probabilities across octaves are not the same
Melody insights #2 Swarams close to each other are more melodious The rules of Arohanam, Avarohanam are encoded We begin to see gamakams Sometimes, the in-between is worse than either extreme
First Order Matrix https://en.wikipedia.org/wiki/Markov_chain#Music Markov Chains in Music https://github.com/rm-hull/markov-chains Second Order Matrix
What next Model insights as melodic abstractions Use synthesis models with generative music Experiment with Rhythm Synthesise Human Voice Deep learning (Recurrent variational auto encoders)