Music to Score Conversion using Machine Learning

The Music to Score Conversion (MSC) project focuses on bridging the gap between auditory and visual representations of music. It uses signal processing techniques for the conversion such as pitch estimation, onset detection, fourier transform and Constant Q Transform. The study also explores real-time conversion for music education and production, as well as strategies to increase accessibility for individuals with hearing impairments. Rigorous testing on diverse datasets demonstrates the potential of these methodologies to revolutionize various aspects of the music industry. This research not only advances MSC but also offers crucial insights into its transformative impact on musical experiences and inclusivity across diverse audiences and sectors, enabling seamless translation from auditory to visual musical representations.