A Hybrid Deep Learning Model for Evaluation System of Traditional Chinese Music Performance

Ancient Chinese music, with its rich cultural meanings and expressive subtleties, is quite challenging to objectively evaluate because it is interpretative, sensitive to tones, and heterogeneous in terms of style. Current systems of evaluation depend on the expert's subjective feedback or predetermined rubric scores, which are typically associated with inconsistencies, low scalability, and non-tailored treatment. To address these challenges, this research introduces an innovative intelligent assessment system that combined hybrid model to evaluate musical performances accurately and with stylistic sensitivity. The system receives audio inputs through sophisticated signal processing methods, extracting the most important musical features like pitch accuracy, rhythm stability, timbre tone, and dynamic intensity. These characteristics are subsequently examined by the deep learning model that has been trained on expert-annotated traditional Chinese music performances, mimicking human-like assessment patterns. Experimental confirmation on the dataset compiled for varied musical content yields excellent results with an accuracy of 96.6%, precision of 96.6%, recall of 96.4%, and an F1-score of 96.5%. This smart system not only increases consistency and interpretability but also provides useful real-time feedback for students and teachers, making it a potential tool for furthering the contemporary pedagogy of Chinese traditional music education.