Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{169970,
        author = {Aakanksha Mishra and Priyanka Gaikwad and Mihir Prasad and Avinash Aryan},
        title = {VITRUAL VIOLIN TUTOR: ML POWERED LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {2768-2772},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169970},
        abstract = {In this paper, we introduce the Virtual Violin Tutor, which makes use of machine learning techniques in offering real-time evaluation and advice for the enhancement of a violinist's performance. Key components included pitch identification, visible fingerboard notation, and live posture analysis. The system uses artificial intelligence in modeling the instructor to provide individual lessons based on performance and development, thereby achieving an engaging and effective learning process. The system approach is detailed, along with the methods and algorithms used, and preliminary user-test results are discussed.},
        keywords = {Machine Learning, Violin Tutor, Posture Analysis, Pitch Detection, Music Education, Fingerboard Notation.},
        month = {November},
        }