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

TL;DR

ragamAI is a novel machine learning framework designed to generate and recommend curated Carnatic music concert sequences, enhancing performance planning and audience experience by leveraging musical structures and past concert data.

Contribution

The paper introduces ragamAI, a new recommender system that models Carnatic concerts using playlist and session-based techniques to improve concert arrangement and music discovery.

Findings

Model performs 25%-50% better than baseline models

Assists musicians in customizing performances

Helps listeners discover a wide range of melodies

Abstract

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.