A long-term memory variational GAN for high-quality music generation and its application in music education

Traditional music generation models suffer from error accumulation and degraded quality in long-sequence generation, limiting their potential applications in intelligent music education. This study proposes a Long-term Memory Variational Generative Adversarial Network (LM-VGAN) by integrating long short-term memory and gated feature selection modules. We constructed multiple baseline models, including VAEGAN for comparative experiments, and adopted both objective quantitative metrics and subjective auditory evaluation to assess model performance. Experimental results show that after 50 iterations, LM-VGAN achieves a scale consistency of 89.15, pitch range of 1055, and note quality of 0.391, which outperforms mainstream baseline models and approaches real music standards. In subjective evaluation, it scores 8.30 in pleasantness and 7.77 in naturalness. The proposed model effectively solves the long-term dependency problem of music sequences. It provides a reliable technical solution for content creation, personalized training, and innovative development in modern potential applications for music education.