Publication Date
12-1-2022
Document Type
Article
Publication Title
Systems and Soft Computing
Volume
4
DOI
10.1016/j.sasc.2022.200042
Abstract
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.
Keywords
Machine Learning, Indian Classical Music, Deep Learning, Long Short-term Memory, Cloud Computing, DevOps
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Applied Data Science
Recommended Citation
Vishnu S. Pendyala, Nupur Yadav, Chetan Kulkarni, and Lokesh Vadlamudi. "Towards building a Deep Learning based Automated Indian Classical Music Tutor for the Masses" Systems and Soft Computing (2022). https://doi.org/10.1016/j.sasc.2022.200042
