@article{4587,
  author = {Meifan Ma},
  title = {AI-Driven Music Composition Using Recursive Neural Networks},
  journal = {Journal of Networking Technology},
  year = {2025},
  volume = {16},
  number = {4},
  doi = {https://doi.org/10.6025/jnt/2025/16/4/147-154},
  url = {https://www.dline.info/jnt/fulltext/v16n4/jntv16n4_1.pdf},
  abstract = {This paper presents an AI driven digital music creation system based on recursive neural networks (RNNs), designed to automate and enhance the music composition process. By leveraging RNNs particularly architectures like LSTM the system learns patterns from MIDI training data to generate new musical pieces that reflect specific styles, rhythms, and harmonic structures. The approach addresses the complexity and unpredictability of traditional digital music creation by reducing reliance on manual input and musical expertise. The study outlines the neural network’s architecture, training process, and the use of activation functions, such as Sigmoid, alongside feature selection and data preprocessing techniques to enhance performance. Experimental results show that processed data yields an 81.2% classification accuracy, rising to 90% with paragraph based feature methods. The system demonstrates strong potential in applications such as music education and live performance, though challenges remain in expressing emotional depth and ensuring creative diversity. Future work includes integrating large language models, such as ChatGPT, to enable expressive and improvisational AI musicians.},
}