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The project involves developing an AI-powered tool for translating music scores into Braille, enhancing accessibility for visually impaired musicians. By leveraging machine learning technologies, the tool aims to simplify the transcription process, reduce the dependency on expert proofreaders, and provide a more inclusive and independent musical experience for the blind and low vision community.
Visually impaired musicians face significant barriers in accessing music scores due to the scarcity of Braille music proofreaders and the complexities involved in traditional transcription methods. This project addresses the critical need for a reliable, efficient, and user-friendly system that can autonomously convert standard music notation into accurate Braille format.
The primary goal of this project is to create a robust AI tool that can accurately translate music scores into Braille without human intervention. Objectives include enhancing the precision of Braille translations, improving the user experience for visually impaired users through a tailored web interface, and fostering a community for user feedback and continuous improvement of the tool.
The project faces limitations including the current technological constraints of AI in fully understanding complex musical nuances, the potential need for initial human oversight in the translation process, and the challenge of integrating high-level AI functionalities into user-friendly web platforms. Additionally, time and resource constraints may limit the scope of initial releases.
The anticipated outcome of the project is a scalable, accessible AI tool that significantly improves the accessibility of music for visually impaired individuals. The tool is expected to reduce the dependency on scarce Braille music proofreaders and empower visually impaired musicians to explore, learn, and enjoy music more independently and comprehensively.
This thesis explores the development of an AI-powered tool designed to enhance music accessibility for the visually impaired by automating the transcription of music scores into Braille. Traditional methods of Braille music transcription require skilled proofreaders and are fraught with barriers that limit accessibility for blind and low vision musicians.
Traditionally, music transcription into Braille has been a complex process, requiring skilled proofreaders to ensure accuracy.The lack of proofreaders has been a significant barrier, making music less accessible to those who need it most. Music transcription into Braille is a fundamental gateway to inclusivity within the arts, opening doors for the visually impaired musician to actively participate in learning, performing, and appreciating music. This mission, while noble, is fraught with challenges that hinder its effectiveness and reach.
In New York alone, among the 200,000 individuals with blindness or low vision, there are merely 2 proofreaders dedicated to braille music. This scarcity underscores a critical bottleneck in the process, given the indispensable role of proofreaders in ensuring the accuracy of braille music translations.
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