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South Indian classical music (Carnatic music) is best consumed through liveconcerts. A carnatic recital requires meticulous planning accounting forseveral parameters like the performers' repertoire, composition variety,musical versatility, thematic structure, the recital's arrangement, etc. toensure that the audience have a comprehensive listening experience. In thiswork, we present ragamAI a novel machine learning framework that utilizes thetonic nuances and musical structures in the carnatic music to generate aconcert 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 concertsand recommends relevant items for the playlist/concert. ragamAI ensemblesrecommendations given by multiple models to learn user idea and past preferenceof sequences in concerts to extract recommendations. Our experiments on a vastcollection of concert show that our model performs 25%-50% better than baselinemodels. ragamAI's applications are two-fold. 1) it will assist musicians tocustomize their performance with the necessary variety required to sustain theinterest of the audience for the entirety of the concert 2) it will generatecarefully curated lists of south Indian classical music so that the listenercan discover the wide range of melody that the musical system can offer.

ragamAI: A Network Based Recommender System to Arrange a Indian Classical Music Concert