Harmonizing Tradition with Innovation: A Deep Learning-Powered Personalized Erhu Teaching Experience

Academic efforts to enhance erhu instruction reflect a wider educational reform integrating advanced technologies and innovative frameworks to enrich traditional Chinese musical instrument learning. Deep learning’s role in erhu education promises to revolutionize teaching methods through personalized learning paths and adaptive strategies, enhancing student experiences. The study focuses on the construction of a personalized erhu teaching system based on deep learning. It mentions the utilization of deep learning for offering customized learning paths and adaptive teaching frameworks, aiming to improve the quality of music education and provide students with personalized learning experience. The optimal configuration led to a model with 203,338 trainable parameters, achieving an impressive 93.87% accuracy. This high accuracy, demonstrated through detailed training/validation loss and accuracy plots over 150 epochs to prevent over fitting, and a confusion matrix with minimal classifications, underscores the potential of deep learning in enhancing music genre classification methodologies.