Multi-Track Music Generation Based on the AC Algorithm and Global Value Return Network

In the current field of deep learning and music information retrieval, automated music generation has become a hot research topic. This study addresses the issues of low clarity and musicality in current multi-track music generation by combining the Actor-Critic algorithm and the Global Value Return Network to create a novel multi-track music generation model. The study first utilizes the Actor-Critic algorithm to generate single-track music rhythm and melody models. Building upon this foundation, the study further optimizes the single-track models using the Global Value Return Network and proposes the multi-track music model. The results demonstrate that the harmonization accuracy of the final multi-track music generation model ranges from 0.90 to 0.98, with a maximum value of 0.98. Additionally, the audience satisfaction and expert satisfaction of the model are 0.96 and 0.97, respectively, indicating that the model has a high musical appreciation value. Overall, the multi-track music generation model designed in this study addresses the limitations of single-track music generation and produces more rhythmically diverse multi-track music.

@article{Guo2024,
  title     = {Multi-Track Music Generation Based on the AC Algorithm and Global Value Return Network},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {3},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Wei Guo},
  doi       = {10.14569/IJACSA.2024.0150346},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150346}
}