Comparative Analysis of cVAE, cGAN, and LSTM Models for Music Generation Based on Weather Conditions

References

1. A. van den Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, and K. Kavukcuoglu, “WaveNet: A Generative Model for Raw Audio,” 2016.
2. G. Hadjeres, F. Pachet, and F. Nielsen, “DeepBach: a Steerable Model for Bach Chorales Generation,” 2017.
3. J. Engel, K. K. Agrawal, S. Chen, I. Gulrajani, C. Donahue, and A. Roberts, “GANSynth: Adversarial Neural Audio Synthesis,” 2019.
4. Z. Zheng, C. Cai, and Y. Zhang, “Real-Time Intelligent Big Data Processing: Technology, Platform, and Applications,” 2019.
5. A. N. Navaz and T. Karthikeyan, “Real-Time Data Streaming Algorithms and Processing Technologies: A Survey,” 2019.
6. J.-P. Briot, G. Hadjeres, and F.-D. Pachet, “Deep Learning Techniques for Music Generation,” 2020.
7. P. Dhariwal, H. Jun, C. Payne, J. W. Kim, A. Radford, and I. Sutskever, “Jukebox: A Generative Model for Music,” 2020.
8. W. X. Zhao, Y. Wu, J. Liu, and J. Xu, “Retrieval-Augmented Generation for AI-Generated Content: A Survey,” 2023.
9. J. Wen and K. M. Ting, “Computational Intelligence Techniques for Music Composition: A Review,” 2023.
10. Google Cloud, “Enabling Real-Time AI with Streaming Ingestion in Vertex AI,” 2022. [Online]. Available: https://cloud.google.com/blog/products/ai-machine-learning/real-time-ai-with-streaming-ingestion-in-vertex-ai
11. Y. Zhao, X. Wang, and C. Liu, “Domain Adversarial Training on Conditional VAE for Controllable Music Generation,” 2022.
12. D. Conner, T. Johnson, and K. Lee, “Music Generation Using LSTM Networks for Sequence Modeling,” 2022.
13. Lakh MIDI Dataset.
14. Historical Hourly Weather Data 2012-2017

Cited by

1 — Crossref — cited-by count (retrieved 2 Oct 2026, source)

Citation counts are shown with their source and retrieval date. They are not an Impact Factor or any journal-level metric.