  South Indian classical music (Carnatic music) is best consumed through live
concerts. A carnatic recital requires meticulous planning accounting for
several parameters like the performers' repertoire, composition variety,
musical versatility, thematic structure, the recital's arrangement, etc. to
ensure that the audience have a comprehensive listening experience. In this
work, we present ragamAI a novel machine learning framework that utilizes the
tonic nuances and musical structures in the carnatic music to generate a
concert recital that melodically captures the entire range in an octave.
Utilizing the underlying idea of playlist and session-based recommender models,
the proposed model studies the mathematical structure present in past concerts
and recommends relevant items for the playlist/concert. ragamAI ensembles
recommendations given by multiple models to learn user idea and past preference
of sequences in concerts to extract recommendations. Our experiments on a vast
collection of concert show that our model performs 25%-50% better than baseline
models. ragamAI's applications are two-fold. 1) it will assist musicians to
customize their performance with the necessary variety required to sustain the
interest of the audience for the entirety of the concert 2) it will generate
carefully curated lists of south Indian classical music so that the listener
can discover the wide range of melody that the musical system can offer.
