Research on an Intelligent Evaluation System for Sight-Singing and Ear Training Based on Deep Learning

This study describes the design and creation of an intelligent, deep-learning-based evaluation of the sight-singing and ear-training assessment, which is an area of a longstanding gap in music education where evaluation has been based on human subjectivity and analysis of one skill at a time. The suggested system is a combination of four fundamental dimensions of musicianship, namely pitch accuracy, rhythmic precision, interval recognition, and solfege articulation, on a single multimodal fusion framework using convolutional neural networks, Bi LSTM temporal models, Transformer encoders, and spectral-temporal feature alignment. The system was trained and tested on a large, multi-level data consisting of beginner to expert vocal performances to be trained and tested on a wide scale of singing styles, skill patterns, and acoustic conditions. Experimental findings indicate high performance improvement against state-of-the-art models with a 14.2 cent pitch error, 28 ms rhythmic onset error, 92 percent interval accuracy and 90 percent solfege rating and a score correlation of 0.94 with expert human raters. Not only does the system provide high precision scoring, it also produces diagnostic as well as skill specific feedback that is pedagogically relevant in indicating the strengths and weaknesses of learners. Relative analysis has shown that the model is better than the current commercial and research systems and it is deeper, reliable, and has more granularity in analysis. The multimodal would have to be able to neutralize distracting noises as well as the varying levels of timbre and timing in an expressive rendition, and would thus have to be employed in the actual teaching environment and not necessarily in the highly controlled laboratory setting. The study presents an extensive framework that enables more sophisticated automated evaluation and assessment in music, sets the foundation for adaptive music learning training systems, supports real-time analytics of music performances, and allows for integrating more broadly the use of AI-based evaluations into music education.