AI-Driven ChatGPT Applications for Enhancing Music Education

Dut to the limitations inherent in traditional music education approaches, the remarkable predictive and analytical prowess of artificial intelligence has been integrated to amplify the quality of music education. This paper investigates the integration of ChatGPT, an emerging machine learning technology based on deep learning and Natural Language Processing, with music education, and analyzes various approaches. These include the utilization of contemporary spectral analysis techniques to deconstruct songs and the adoption of three deep neural network models: SIConvNe, JointEmbedNe, and DistMatNet, to evaluate music. By combining the GPT architecture with Constraint Satisfaction Programming, ABC notation, and Domain-Specific Language technology frameworks, differentiated music exercises are generated. By employing a Large Language Model, centered around the LLaMA2 model, and refining its capabilities through ChatGPT pre-training and fine-tuning, the understanding and generation of music are continually enhanced. Finally, to address the challenges of inadequate explainability, algorithmic limitations, and low applicability in this field, this study discussed the combination of domain expertise with expert systems for model training, and employs Layer-wise Relevance Propagation and Local Interpretable Model-Agnostic Explanations to increase system transparency. Integrating audio analysis and visualization technologies, expanding system capabilities to accommodate a broader range of exercises, and developing algorithms to facilitate more interactive and personalized learning tools all serve to bolster the system's adaptability and flexibility. Utilizing domain-adaptive techniques, transfer learning, and multi-task learning methods, the GPT model's adaptability and generalization capability are strengthened, enabling it to skillfully generate pieces in varying styles based on a broader music corpus.