Towards building a Deep Learning based Automated Indian Classical Music Tutor for the Masses

Music can play an important role in the well-being of the world. Indian classical music is unique in its requirement for rigorous, disciplined, expert-led training that typically goes on for years before the learner can reach a reasonable level of performance. This keeps many, including the first author of this paper, away from mastering the skill. The problem is particularly compounded in rural areas, where the available expertise may be limited and prohibitively expensive, but the interest in learning classical music still prevails, nevertheless. Machine Learning has been complementing, enhancing, and replacing many white-collar jobs and we believe it can help with this problem as well. This paper describes efforts at using Machine Learning techniques, particularly, Long Short-Term Memory for building a system that is a step toward provisioning an Indian Classical Music Tutor for the masses. The system is deployed in the cloud using orchestrated containerization for potential worldwide access, load balancing, and other robust features

On the Distributional Representation of Ragas: Experiments with Allied Raga Pairs

Raga grammar provides a theoretical framework that supports creativity and flexibility in improvisation while carefully maintaining the distinctiveness of each raga in the ears of a listener. A computational model for raga grammar can serve as a powerful tool to characterize grammaticality in performance. Like in other forms of tonal music, a distributional representation capturing tonal hierarchy has been found to be useful in characterizing a raga’s distinctiveness in performance. In the continuous-pitch melodic tradition, several choices arise for the defining attributes of a histogram representation of pitches. These can be resolved by referring to one of the main functions of the representation, namely to embody the raga grammar and therefore the technical boundary of a raga in performance. Based on the analyses of a representative dataset of audio performances in allied ragas by eminent Hindustani vocalists, we propose a computational representation of distributional information, and further apply it to obtain insights about how this aspect of raga distinctiveness is manifested in practice over different time scales by very creative performers